{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/code/block","entry":"Block","source":"Syntology graph, per-sample; not an archive number","read_at":"2026-09-24T18:15:14+00:00","claim":"Names are grouped by exact entry-name string. Same-named routines are NOT asserted to be equivalent; 'ran' means executed on a synthesized fixture, not correctness. n_samples_ran = sum of by_status over every status except 'unverified' (ran_draft_wrong and ran_fixture are failures of Syntology's instrument, not of the code); n_papers_ran = papers with at least one such sample.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"},"n_papers":252,"n_papers_ran":173,"units":"n_samples, n_samples_ran, n_samples_fingerprinted and by_status count distinct code bodies (code_sha256); n_places and n_places_pointer_only count places, one per (paper, code body) pair, which is also the unit of the samples list","n_samples":309,"n_samples_ran":211,"n_samples_fingerprinted":21,"n_places":309,"n_places_pointer_only":157,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":2,"ran_fixture":0,"ran":209,"unverified":98},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2608.15062","paper":"/paper/arxiv-2608-15062","title":"Gated Recurrent Transformers: Expressive Depth through Recurrent Modulation","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"Amr-Hegazy1/gated-recurrent-transformer","path":"model.py","file_url":"https://github.com/Amr-Hegazy1/gated-recurrent-transformer/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4bf62a544098285f","mcp_get_code":{"code_sha256":"4bf62a544098285f"}},{"arxiv_id":"2608.07249","paper":"/paper/arxiv-2608-07249","title":"Stoicheia: Character-Level Masked Diffusion for Ancient Greek Textual Restoration, Parsing, and Metrical Scansion","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"ericu9500/stoicheia","path":"hf_release/modeling_char_bert_joint.py","file_url":"https://github.com/ericu9500/stoicheia/blob/HEAD/hf_release/modeling_char_bert_joint.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"83e8814857ba170e","mcp_get_code":{"code_sha256":"83e8814857ba170e"}},{"arxiv_id":"2606.30875","paper":"/paper/arxiv-2606-30875","title":"The Label Imitation Game: Turing Test Network for Zero-Shot Pseudo-Label Pruning","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"voxel51/ttn","path":"ttn/ttn_model.py","file_url":"https://github.com/voxel51/ttn/blob/HEAD/ttn/ttn_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"206259678d6de891","mcp_get_code":{"code_sha256":"206259678d6de891"}},{"arxiv_id":"2606.27655","paper":"/paper/arxiv-2606-27655","title":"Temporal-Emerged Prompting for Segment Anything in Multiframe Infrared Small Target Detection","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"cdh8285/TEP-SAM","path":"models/sam_withToken.py","file_url":"https://github.com/cdh8285/TEP-SAM/blob/HEAD/models/sam_withToken.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6f8342e3a835af78","mcp_get_code":{"code_sha256":"6f8342e3a835af78"}},{"arxiv_id":"2606.08397","paper":"/paper/arxiv-2606-08397","title":"TrustMargin: Training-Free Arbitration between Parametric Memory and Retrieved Evidence in Large Language Models","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"mojixu/TrustMargin","path":"src/trustmargin.py","file_url":"https://github.com/mojixu/TrustMargin/blob/HEAD/src/trustmargin.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7b64fbe122b83e35","mcp_get_code":{"code_sha256":"7b64fbe122b83e35"}},{"arxiv_id":"2605.14200","paper":"/paper/arxiv-2605-14200","title":"How to Scale Mixture-of-Experts: From µP to the Maximally Scale-Stable Parameterization","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"vankadara-lab/mssp-moe","path":"transformer-moe-experiments/model.py","file_url":"https://github.com/vankadara-lab/mssp-moe/blob/HEAD/transformer-moe-experiments/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6a082740ae6a704b","mcp_get_code":{"code_sha256":"6a082740ae6a704b"}},{"arxiv_id":"2605.13989","paper":"/paper/arxiv-2605-13989","title":"VectraYX-Nano: A 42M-Parameter Spanish Cybersecurity Language Model with Curriculum Learning and Native Tool Use","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"vectrayx/vectrayx-nano-paper","path":"training/transformer.py","file_url":"https://github.com/vectrayx/vectrayx-nano-paper/blob/HEAD/training/transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bc552c838aa5c126","mcp_get_code":{"code_sha256":"bc552c838aa5c126"}},{"arxiv_id":"2605.11622","paper":"/paper/arxiv-2605-11622","title":"RNA-FM: Flow-Matching Generative Model for Genome-wide RNA-Seq Prediction","date":null,"month_inferred_from_arxiv_id":"2026-05","title_source":"syntology","repo":"YXSong000/RNA-FM","path":"src/model_RNAFM.py","file_url":"https://github.com/YXSong000/RNA-FM/blob/HEAD/src/model_RNAFM.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f73c3a4a985bc0ff","mcp_get_code":{"code_sha256":"f73c3a4a985bc0ff"}},{"arxiv_id":"2604.12518","paper":"/paper/arxiv-2604-12518","title":"Enhance-then-Balance Modality Collaboration for Robust Multimodal Sentiment Analysis","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"kangverse/EBMC","path":"EBMC/ebmc.py","file_url":"https://github.com/kangverse/EBMC/blob/HEAD/EBMC/ebmc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"0ac815f30b98af6c","mcp_get_code":{"code_sha256":"0ac815f30b98af6c"}},{"arxiv_id":"2604.06155","paper":"/paper/arxiv-2604-06155","title":"Toward Consistent World Models with Multi-Token Prediction and Latent Semantic Enhancement","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"QiminZhong/LSE-MTP","path":"model.py","file_url":"https://github.com/QiminZhong/LSE-MTP/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9b480329d99cb4a9","mcp_get_code":{"code_sha256":"9b480329d99cb4a9"}},{"arxiv_id":"2604.04420","paper":"/paper/arxiv-2604-04420","title":"Is Prompt Selection Necessary for Task-Free Online Continual Learning?","date":null,"month_inferred_from_arxiv_id":"2026-04","title_source":"syntology","repo":"efficient-learning-lab/SinglePrompt","path":"models/singlePrompt.py","file_url":"https://github.com/efficient-learning-lab/SinglePrompt/blob/HEAD/models/singlePrompt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9b15f0ec8365d3fe","mcp_get_code":{"code_sha256":"9b15f0ec8365d3fe"}},{"arxiv_id":"2603.24298","paper":"/paper/arxiv-2603-24298","title":"SpinGQE: A generative quantum eigensolver for spin Hamiltonians","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"Mindbeam-AI/SpinGQE","path":"SpinGQE.py","file_url":"https://github.com/Mindbeam-AI/SpinGQE/blob/HEAD/SpinGQE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5221926c1c81e0e0","mcp_get_code":{"code_sha256":"5221926c1c81e0e0"}},{"arxiv_id":"2603.16739","paper":"/paper/arxiv-2603-16739","title":"SpecMoE: Spectral Mixture-of-Experts Foundation Model for Cross-Species EEG Decoding","date":null,"month_inferred_from_arxiv_id":"2026-03","title_source":"syntology","repo":"935963004/LaBraM","path":"modeling_pretrain.py","file_url":"https://github.com/935963004/LaBraM/blob/HEAD/modeling_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b8c87f15b20f0ad5","mcp_get_code":{"code_sha256":"b8c87f15b20f0ad5"}},{"arxiv_id":"2602.22555","paper":"/paper/arxiv-2602-22555","title":"Autoregressive Visual Decoding from EEG Signals","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"ddicee/avde","path":"models/labram.py","file_url":"https://github.com/ddicee/avde/blob/HEAD/models/labram.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b89948f53a64da1d","mcp_get_code":{"code_sha256":"b89948f53a64da1d"}},{"arxiv_id":"2602.17559","paper":"/paper/arxiv-2602-17559","title":"Revisiting Weight Regularization for Low-Rank Continual Learning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"yaoyz96/low-rank-cl","path":"methods/ewclora.py","file_url":"https://github.com/yaoyz96/low-rank-cl/blob/HEAD/methods/ewclora.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b99f52d9905d2c4e","mcp_get_code":{"code_sha256":"b99f52d9905d2c4e"}},{"arxiv_id":"2602.16951","paper":"/paper/arxiv-2602-16951","title":"BrainRVQ: A High-Fidelity EEG Foundation Model via Dual-Domain Residual Quantization and Hierarchical Autoregression","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"keqicmz/BrainRVQ","path":"DDRVQ/modeling_ddrvq.py","file_url":"https://github.com/keqicmz/BrainRVQ/blob/HEAD/DDRVQ/modeling_ddrvq.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"843b52cb643f99ac","mcp_get_code":{"code_sha256":"843b52cb643f99ac"}},{"arxiv_id":"2602.08064","paper":"/paper/arxiv-2602-08064","title":"SiameseNorm: Breaking the Barrier to Reconciling Pre/Post-Norm","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Qwen-Applications/SiameseNorm","path":"deit-model-siamesenorm.py","file_url":"https://github.com/Qwen-Applications/SiameseNorm/blob/HEAD/deit-model-siamesenorm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e6741f8f4d72b26","mcp_get_code":{"code_sha256":"4e6741f8f4d72b26"}},{"arxiv_id":"2602.05389","paper":"/paper/arxiv-2602-05389","title":"A Decomposition-based State Space Model for Multivariate Time-Series Forecasting","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"Neurogica/DecompSSM","path":"models/decompssm/network.py","file_url":"https://github.com/Neurogica/DecompSSM/blob/HEAD/models/decompssm/network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause-Clear","inline_ok":false,"code_sha256_prefix":"0052a53c75a293b0","mcp_get_code":{"code_sha256":"0052a53c75a293b0"}},{"arxiv_id":"2602.05387","paper":"/paper/arxiv-2602-05387","title":"Parallel Swin Transformer-Enhanced 3D MRI-to-CT Synthesis for MRI-Only Radiotherapy Planning","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"mobaidoctor/med2transformer","path":"utils.py","file_url":"https://github.com/mobaidoctor/med2transformer/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2ac23cc0d95d48ec","mcp_get_code":{"code_sha256":"2ac23cc0d95d48ec"}},{"arxiv_id":"2602.03473","paper":"/paper/arxiv-2602-03473","title":"Scaling Continual Learning to 300+ Tasks with Bi-Level Routing Mixture-of-Experts","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"LMMMEng/CaRE","path":"backbone/vit_brmoe.py","file_url":"https://github.com/LMMMEng/CaRE/blob/HEAD/backbone/vit_brmoe.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5a863df7a76f7e2","mcp_get_code":{"code_sha256":"e5a863df7a76f7e2"}},{"arxiv_id":"2602.02603","paper":"/paper/arxiv-2602-02603","title":"EchoJEPA: A Latent Predictive Foundation Model for Echocardiography","date":null,"month_inferred_from_arxiv_id":"2026-02","title_source":"syntology","repo":"bowang-lab/EchoJEPA","path":"src/models/predictor.py","file_url":"https://github.com/bowang-lab/EchoJEPA/blob/HEAD/src/models/predictor.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0785f467b1ee8f5f","mcp_get_code":{"code_sha256":"0785f467b1ee8f5f"}},{"arxiv_id":"2601.21031","paper":"/paper/arxiv-2601-21031","title":"SIGMA-PPG: Statistical-prior Informed Generative Masking Architecture for PPG Foundation Model","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ZonghengGuo/SigmaPPG","path":"pretraining/pretraining_model.py","file_url":"https://github.com/ZonghengGuo/SigmaPPG/blob/HEAD/pretraining/pretraining_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2813dba0a55c4934","mcp_get_code":{"code_sha256":"2813dba0a55c4934"}},{"arxiv_id":"2601.20257","paper":"/paper/arxiv-2601-20257","title":"C2:Cross learning module enhanced decision transformer with Constraint-aware loss for auto-bidding","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Dingjinren/C2","path":"bidding_train_env/baseline/C2.py","file_url":"https://github.com/Dingjinren/C2/blob/HEAD/bidding_train_env/baseline/C2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1c71b701cb49c56f","mcp_get_code":{"code_sha256":"1c71b701cb49c56f"}},{"arxiv_id":"2601.17883","paper":"/paper/arxiv-2601-17883","title":"EEG-FM-Compass: Progress, Benchmarking, and Future Directions for EEG Foundation Models","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"Dingkun0817/EEG-FM-Benchmark","path":"models/FM/EEGPT/Model_EEGPT.py","file_url":"https://github.com/Dingkun0817/EEG-FM-Benchmark/blob/HEAD/models/FM/EEGPT/Model_EEGPT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6399e309ecb84ffc","mcp_get_code":{"code_sha256":"6399e309ecb84ffc"}},{"arxiv_id":"2601.15771","paper":"/paper/arxiv-2601-15771","title":"Rethinking Drug-Drug Interaction Modeling as Generalizable Relation Learning","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"SZU-ADDG/GenRel-DDI","path":"model.py","file_url":"https://github.com/SZU-ADDG/GenRel-DDI/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4ffc821cea0a58ae","mcp_get_code":{"code_sha256":"4ffc821cea0a58ae"}},{"arxiv_id":"2601.00459","paper":"/paper/arxiv-2601-00459","title":"Combining Residual U-Net and Data Augmentation for Dense Temporal Segmentation of Spike Wave Discharges in Single-Channel EEG","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"lu-wo/DETRtime","path":"DETRtime/model/backbones/UNet.py","file_url":"https://github.com/lu-wo/DETRtime/blob/HEAD/DETRtime/model/backbones/UNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4cf898ad0e04dc11","mcp_get_code":{"code_sha256":"4cf898ad0e04dc11"}},{"arxiv_id":"2512.04341","paper":"/paper/arxiv-2512-04341","title":"Long-Horizon Model-Based Offline Reinforcement Learning Without Explicit Conservatism","date":null,"month_inferred_from_arxiv_id":"2025-12","title_source":"syntology","repo":"proroklab/memoroids","path":"modules.py","file_url":"https://github.com/proroklab/memoroids/blob/HEAD/modules.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0cd85f17db6bef74","mcp_get_code":{"code_sha256":"0cd85f17db6bef74"}},{"arxiv_id":"2511.07222","paper":"/paper/arxiv-2511-07222","title":"Omni-View: Unlocking How Generation Facilitates Understanding in Unified 3D Model based on Multiview images","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"AIDC-AI/Omni-View","path":"modeling/bagel/bagel.py","file_url":"https://github.com/AIDC-AI/Omni-View/blob/HEAD/modeling/bagel/bagel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e093c7840714cbc5","mcp_get_code":{"code_sha256":"e093c7840714cbc5"}},{"arxiv_id":"2510.16446","paper":"/paper/arxiv-2510-16446","title":"VIPAMIN: Visual Prompt Initialization via Embedding Selection and Subspace Expansion","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"iamjaekyun/vipamin","path":"src/models/vit_prompt/vit_exp_self.py","file_url":"https://github.com/iamjaekyun/vipamin/blob/HEAD/src/models/vit_prompt/vit_exp_self.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0015921a7e8969dc","mcp_get_code":{"code_sha256":"0015921a7e8969dc"}},{"arxiv_id":"2510.11321","paper":"/paper/arxiv-2510-11321","title":"HiMaCon: Discovering Hierarchical Manipulation Concepts from Unlabeled Multi-Modal Data","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"zrllrz/HiMaCon","path":"src/hminfocon.py","file_url":"https://github.com/zrllrz/HiMaCon/blob/HEAD/src/hminfocon.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cdfcfd022d0679e9","mcp_get_code":{"code_sha256":"cdfcfd022d0679e9"}},{"arxiv_id":"2510.06691","paper":"/paper/arxiv-2510-06691","title":"Latent Representation Learning in Heavy-Ion Collisions with MaskPoint Transformer","date":null,"month_inferred_from_arxiv_id":"2025-10","title_source":"syntology","repo":"Giovanni-Sforza/MaskPoint-AMPT","path":"models/MaskPoint.py","file_url":"https://github.com/Giovanni-Sforza/MaskPoint-AMPT/blob/HEAD/models/MaskPoint.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"4d94ec6274a40f2c","mcp_get_code":{"code_sha256":"4d94ec6274a40f2c"}},{"arxiv_id":"2509.25033","paper":"/paper/arxiv-2509-25033","title":"VT-FSL: Bridging Vision and Text with LLMs for Few-Shot Learning","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"peacelwh/VT-FSL","path":"model/visformer.py","file_url":"https://github.com/peacelwh/VT-FSL/blob/HEAD/model/visformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35404f99093e9ae8","mcp_get_code":{"code_sha256":"35404f99093e9ae8"}},{"arxiv_id":"2509.24693","paper":"/paper/arxiv-2509-24693","title":"Brain Harmony: A Multimodal Foundation Model Unifying Morphology and Function into 1D Tokens","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"hzlab/Brain-Harmony","path":"modules/harmonizer/stage1_pretrain/models.py","file_url":"https://github.com/hzlab/Brain-Harmony/blob/HEAD/modules/harmonizer/stage1_pretrain/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6fdfbb3c57d4f9fe","mcp_get_code":{"code_sha256":"6fdfbb3c57d4f9fe"}},{"arxiv_id":"2509.20609","paper":"/paper/arxiv-2509-20609","title":"MMG: Mutual Information Estimation via the MMSE Gap in Diffusion","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"fengsxy/Diffusion-MI","path":"src/estimators/neural/MMG.py","file_url":"https://github.com/fengsxy/Diffusion-MI/blob/HEAD/src/estimators/neural/MMG.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cf2ffc332449c471","mcp_get_code":{"code_sha256":"cf2ffc332449c471"}},{"arxiv_id":"2508.14588","paper":"/paper/arxiv-2508-14588","title":"Controllable Latent Space Augmentation for Digital Pathology","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"MICS-Lab/HistAug","path":"src/histaug/models/histaug_model.py","file_url":"https://github.com/MICS-Lab/HistAug/blob/HEAD/src/histaug/models/histaug_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"CC-BY-4.0","inline_ok":false,"code_sha256_prefix":"ab04857ebc5c8e0b","mcp_get_code":{"code_sha256":"ab04857ebc5c8e0b"}},{"arxiv_id":"2507.00698","paper":null,"title":"arXiv:2507.00698","date":null,"month_inferred_from_arxiv_id":"2025-07","title_source":null,"repo":"qhfan/MALA","path":"classfication_release/model.py","file_url":"https://github.com/qhfan/MALA/blob/HEAD/classfication_release/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ba852edc0f7d6e68","mcp_get_code":{"code_sha256":"ba852edc0f7d6e68"}},{"arxiv_id":"2506.19935","paper":"/paper/any-order-gpt-as-masked-diffusion-model","title":"Any-Order GPT as Masked Diffusion Model: Decoupling Formulation and Architecture","date":"2025-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scxue/AO-GPT-MDM","path":"model_AOGPT_AdaLN6_NoRep_cond_128_trunc_qknorm.py","file_url":"https://github.com/scxue/AO-GPT-MDM/blob/HEAD/model_AOGPT_AdaLN6_NoRep_cond_128_trunc_qknorm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad3472bf14169653","mcp_get_code":{"code_sha256":"ad3472bf14169653"}},{"arxiv_id":"2505.23180","paper":"/paper/proximal-algorithm-unrolling-flexible-and","title":"Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging","date":"2025-05-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pwangcs/ProxUnroll","path":"model/proxunroll.py","file_url":"https://github.com/pwangcs/ProxUnroll/blob/HEAD/model/proxunroll.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"06f1537a5c3bfffe","mcp_get_code":{"code_sha256":"06f1537a5c3bfffe"}},{"arxiv_id":"2505.22815","paper":"/paper/imts-is-worth-time-times-channel-patches","title":"IMTS is Worth Time $\\times$ Channel Patches: Visual Masked Autoencoders for Irregular Multivariate Time Series Prediction","date":"2025-05-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whu-hzy/vimts","path":"IMTS/lib/models/visionts/models_mae.py","file_url":"https://github.com/whu-hzy/vimts/blob/HEAD/IMTS/lib/models/visionts/models_mae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f4ee7167fad69080","mcp_get_code":{"code_sha256":"f4ee7167fad69080"}},{"arxiv_id":"2504.14587","paper":"/paper/generative-auto-bidding-with-value-guided","title":"Generative Auto-Bidding with Value-Guided Explorations","date":"2025-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"applied-machine-learning-lab/gave","path":"code/bidding_train_env/baseline/dt/dt.py","file_url":"https://github.com/applied-machine-learning-lab/gave/blob/HEAD/code/bidding_train_env/baseline/dt/dt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3203b47e7dbd1fd8","mcp_get_code":{"code_sha256":"3203b47e7dbd1fd8"}},{"arxiv_id":"2504.13065","paper":"/paper/echoworld-learning-motion-aware-world-models","title":"EchoWorld: Learning Motion-Aware World Models for Echocardiography Probe Guidance","date":"2025-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/EchoWorld","path":"finetune/models/lvm_med.py","file_url":"https://github.com/LeapLabTHU/EchoWorld/blob/HEAD/finetune/models/lvm_med.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"beaafdedb34a8817","mcp_get_code":{"code_sha256":"beaafdedb34a8817"}},{"arxiv_id":"2504.11054","paper":"/paper/zero-shot-whole-body-humanoid-control-via","title":"Zero-Shot Whole-Body Humanoid Control via Behavioral Foundation Models","date":"2025-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/metamotivo","path":"metamotivo/fb/model.py","file_url":"https://github.com/facebookresearch/metamotivo/blob/HEAD/metamotivo/fb/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"01f0428c88bde5de","mcp_get_code":{"code_sha256":"01f0428c88bde5de"}},{"arxiv_id":"2504.03587","paper":"/paper/autossvh-exploring-automated-frame-sampling","title":"AutoSSVH: Exploring Automated Frame Sampling for Efficient Self-Supervised Video Hashing","date":"2025-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EliSpectre/CVPR25-AutoSSVH","path":"model/AutoSSVH.py","file_url":"https://github.com/EliSpectre/CVPR25-AutoSSVH/blob/HEAD/model/AutoSSVH.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6979c9643a75f11","mcp_get_code":{"code_sha256":"b6979c9643a75f11"}},{"arxiv_id":"2504.01941","paper":"/paper/end-to-end-driving-with-online-trajectory","title":"End-to-End Driving with Online Trajectory Evaluation via BEV World Model","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyingyanucas/wote","path":"navsim/agents/WoTE/WoTE_model.py","file_url":"https://github.com/liyingyanucas/wote/blob/HEAD/navsim/agents/WoTE/WoTE_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c6f24fb57dd225b8","mcp_get_code":{"code_sha256":"c6f24fb57dd225b8"}},{"arxiv_id":"2503.19331","paper":"/paper/cha-maevit-unifying-channel-aware-masked","title":"ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning","date":"2025-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaudatascience/cha_mae_vit","path":"models/cha_mae_vit.py","file_url":"https://github.com/chaudatascience/cha_mae_vit/blob/HEAD/models/cha_mae_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46297c90ff2f0ed7","mcp_get_code":{"code_sha256":"46297c90ff2f0ed7"}},{"arxiv_id":"2503.16997","paper":"/paper/steady-progress-beats-stagnation-mutual-aid","title":"Steady Progress Beats Stagnation: Mutual Aid of Foundation and Conventional Models in Mixed Domain Semi-Supervised Medical Image Segmentation","date":"2025-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MQinghe/SynFoC","path":"code/sam_lora_image_encoder.py","file_url":"https://github.com/MQinghe/SynFoC/blob/HEAD/code/sam_lora_image_encoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1d2032d4479c3f11","mcp_get_code":{"code_sha256":"1d2032d4479c3f11"}},{"arxiv_id":"2503.16689","paper":"/paper/wavefm-a-high-fidelity-and-efficient-vocoder","title":"WaveFM: A High-Fidelity and Efficient Vocoder Based on Flow Matching","date":"2025-03-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"luotianze666/wavefm","path":"src/models.py","file_url":"https://github.com/luotianze666/wavefm/blob/HEAD/src/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1596505e06f8328a","mcp_get_code":{"code_sha256":"1596505e06f8328a"}},{"arxiv_id":"2503.15141","paper":null,"title":"arXiv:2503.15141","date":null,"month_inferred_from_arxiv_id":"2025-03","title_source":null,"repo":"djukicn/ocebo","path":"models/ocebo.py","file_url":"https://github.com/djukicn/ocebo/blob/HEAD/models/ocebo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a07ad99e5706cb12","mcp_get_code":{"code_sha256":"a07ad99e5706cb12"}},{"arxiv_id":"2503.12295","paper":"/paper/towards-learning-high-precision-least-squares","title":"Towards Learning High-Precision Least Squares Algorithms with Sequence Models","date":"2025-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HazyResearch/precision-ls","path":"src/models/gpt2.py","file_url":"https://github.com/HazyResearch/precision-ls/blob/HEAD/src/models/gpt2.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a0745a160f92b244","mcp_get_code":{"code_sha256":"a0745a160f92b244"}},{"arxiv_id":"2503.12035","paper":"/paper/mos-modeling-object-scene-associations-in","title":"MOS: Modeling Object-Scene Associations in Generalized Category Discovery","date":"2025-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jethropeng/mos","path":"network.py","file_url":"https://github.com/jethropeng/mos/blob/HEAD/network.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9324f85dd61da578","mcp_get_code":{"code_sha256":"9324f85dd61da578"}},{"arxiv_id":"2503.10252","paper":"/paper/svip-semantically-contextualized-visual","title":"SVIP: Semantically Contextualized Visual Patches for Zero-Shot Learning","date":"2025-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uqzhichen/SVIP","path":"models/vit_model.py","file_url":"https://github.com/uqzhichen/SVIP/blob/HEAD/models/vit_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"476f4f567601d19b","mcp_get_code":{"code_sha256":"476f4f567601d19b"}},{"arxiv_id":"2503.00205","paper":"/paper/analoggenie-a-generative-engine-for-automatic","title":"AnalogGenie: A Generative Engine for Automatic Discovery of Analog Circuit Topologies","date":"2025-02-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xz-group/analoggenie","path":"Models/GPT.py","file_url":"https://github.com/xz-group/analoggenie/blob/HEAD/Models/GPT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b747b1699e149903","mcp_get_code":{"code_sha256":"b747b1699e149903"}},{"arxiv_id":"2502.02834","paper":"/paper/task-aware-virtual-training-enhancing","title":"Task-Aware Virtual Training: Enhancing Generalization in Meta-Reinforcement Learning for Out-of-Distribution Tasks","date":"2025-02-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunwoolee0504/wall","path":"src/modules/step/models.py","file_url":"https://github.com/sunwoolee0504/wall/blob/HEAD/src/modules/step/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"0340d1b0f37688a5","mcp_get_code":{"code_sha256":"0340d1b0f37688a5"}},{"arxiv_id":"2502.02257","paper":"/paper/unip-rethinking-pre-trained-attention","title":"UNIP: Rethinking Pre-trained Attention Patterns for Infrared Semantic Segmentation","date":"2025-02-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"casiatao/unip","path":"UNIP_pretraining/models_unip.py","file_url":"https://github.com/casiatao/unip/blob/HEAD/UNIP_pretraining/models_unip.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7070415ec38f0594","mcp_get_code":{"code_sha256":"7070415ec38f0594"}},{"arxiv_id":"2501.18936","paper":"/paper/adaptive-prompt-unlocking-the-power-of-visual","title":"Adaptive Prompt: Unlocking the Power of Visual Prompt Tuning","date":"2025-01-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Minhchuyentoancbn/VAPT","path":"src/models/vit_prompt/vit.py","file_url":"https://github.com/Minhchuyentoancbn/VAPT/blob/HEAD/src/models/vit_prompt/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e51dc2d64fc3c533","mcp_get_code":{"code_sha256":"e51dc2d64fc3c533"}},{"arxiv_id":"2501.18768","paper":"/paper/diversity-by-design-leveraging-distribution","title":"Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization","date":"2025-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michael-s-yao/dynamo","path":"src/dynamo/core/dynamo_core.py","file_url":"https://github.com/michael-s-yao/dynamo/blob/HEAD/src/dynamo/core/dynamo_core.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e4406eb4baa4714c","mcp_get_code":{"code_sha256":"e4406eb4baa4714c"}},{"arxiv_id":"2501.18768","paper":"/paper/diversity-by-design-leveraging-distribution","title":"Diversity By Design: Leveraging Distribution Matching for Offline Model-Based Optimization","date":"2025-01-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michael-s-yao/gabo","path":"models/block.py","file_url":"https://github.com/michael-s-yao/gabo/blob/HEAD/models/block.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d88a745600330ee8","mcp_get_code":{"code_sha256":"d88a745600330ee8"}},{"arxiv_id":"2501.13420","paper":"/paper/lvface-large-vision-model-for-face-recogniton","title":"LVFace: Large Vision model for Face Recogniton","date":"2025-01-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/LVFace","path":"backbones/vit.py","file_url":"https://github.com/bytedance/LVFace/blob/HEAD/backbones/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a4232738a67bd47f","mcp_get_code":{"code_sha256":"a4232738a67bd47f"}},{"arxiv_id":"2501.09054","paper":"/paper/neurop-diff-continuous-remote-sensing-image","title":"NeurOp-Diff:Continuous Remote Sensing Image Super-Resolution via Neural Operator Diffusion","date":"2025-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zerono000/NeurOp-Diff","path":"models/diffusion.py","file_url":"https://github.com/zerono000/NeurOp-Diff/blob/HEAD/models/diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ef9719744d2f0aca","mcp_get_code":{"code_sha256":"ef9719744d2f0aca"}},{"arxiv_id":"2501.02030","paper":"/paper/detecting-music-performance-errors-with","title":"Detecting Music Performance Errors with Transformers","date":"2025-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ben2002chou/polytune","path":"tasks/polytune_net.py","file_url":"https://github.com/ben2002chou/polytune/blob/HEAD/tasks/polytune_net.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ddc7d58a33fa5c70","mcp_get_code":{"code_sha256":"ddc7d58a33fa5c70"}},{"arxiv_id":"2412.15032","paper":"/paper/dctdiff-intriguing-properties-of-image","title":"DCTdiff: Intriguing Properties of Image Generative Modeling in the DCT Space","date":"2024-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"forever208/dctdiff","path":"libs/uvit.py","file_url":"https://github.com/forever208/dctdiff/blob/HEAD/libs/uvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8e7a86aeebca3d84","mcp_get_code":{"code_sha256":"8e7a86aeebca3d84"}},{"arxiv_id":"2412.14169","paper":"/paper/autoregressive-video-generation-without","title":"Autoregressive Video Generation without Vector Quantization","date":"2024-12-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"baaivision/nova","path":"diffnext/models/transformers/transformer_nova.py","file_url":"https://github.com/baaivision/nova/blob/HEAD/diffnext/models/transformers/transformer_nova.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a6b18aefa9c30394","mcp_get_code":{"code_sha256":"a6b18aefa9c30394"}},{"arxiv_id":"2412.12095","paper":"/paper/causal-diffusion-transformers-for-generative","title":"Causal Diffusion Transformers for Generative Modeling","date":"2024-12-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"causalfusion/causalfusion","path":"models.py","file_url":"https://github.com/causalfusion/causalfusion/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b6fa5ad782b68160","mcp_get_code":{"code_sha256":"b6fa5ad782b68160"}},{"arxiv_id":"2412.11084","paper":"/paper/barcodemamba-state-space-models-for","title":"BarcodeMamba: State Space Models for Biodiversity Analysis","date":"2024-12-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bioscan-ml/barcodemamba","path":"utils/barcode_mamba.py","file_url":"https://github.com/bioscan-ml/barcodemamba/blob/HEAD/utils/barcode_mamba.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"affa43bc1a64391e","mcp_get_code":{"code_sha256":"affa43bc1a64391e"}},{"arxiv_id":"2412.04786","paper":"/paper/slicing-vision-transformer-for-flexible","title":"Slicing Vision Transformer for Flexible Inference","date":"2024-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BeSpontaneous/Scala-pytorch","path":"models_scala.py","file_url":"https://github.com/BeSpontaneous/Scala-pytorch/blob/HEAD/models_scala.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ab7a1aca590784e","mcp_get_code":{"code_sha256":"3ab7a1aca590784e"}},{"arxiv_id":"2411.16375","paper":"/paper/ca2-vdm-efficient-autoregressive-video","title":"Ca2-VDM: Efficient Autoregressive Video Diffusion Model with Causal Generation and Cache Sharing","date":"2024-11-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"songweige/TATS","path":"tats/tats_transformer.py","file_url":"https://github.com/songweige/TATS/blob/HEAD/tats/tats_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c36e0664ff375f5b","mcp_get_code":{"code_sha256":"c36e0664ff375f5b"}},{"arxiv_id":"2411.11471","paper":"/paper/generalizable-person-re-identification-via-2","title":"Generalizable Person Re-identification via Balancing Alignment and Uniformity","date":"2024-11-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yoonkicho/bau","path":"bau/models/vit.py","file_url":"https://github.com/yoonkicho/bau/blob/HEAD/bau/models/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"acbaef74d8464c1e","mcp_get_code":{"code_sha256":"acbaef74d8464c1e"}},{"arxiv_id":"2411.09702","paper":"/paper/on-the-surprising-effectiveness-of-attention","title":"On the Surprising Effectiveness of Attention Transfer for Vision Transformers","date":"2024-11-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexlioralexli/attention-transfer","path":"models_dual_vit.py","file_url":"https://github.com/alexlioralexli/attention-transfer/blob/HEAD/models_dual_vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"3554200165b73275","mcp_get_code":{"code_sha256":"3554200165b73275"}},{"arxiv_id":"2411.02175","paper":"/paper/safe-slow-and-fast-parameter-efficient-tuning","title":"SAFE: Slow and Fast Parameter-Efficient Tuning for Continual Learning with Pre-Trained Models","date":"2024-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MIFA-Lab/SAFE","path":"petl/vision_transformer_adapter.py","file_url":"https://github.com/MIFA-Lab/SAFE/blob/HEAD/petl/vision_transformer_adapter.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"716ea0db3b61b790","mcp_get_code":{"code_sha256":"716ea0db3b61b790"}},{"arxiv_id":"2410.09633","paper":"/paper/duodiff-accelerating-diffusion-models-with-a","title":"DuoDiff: Accelerating Diffusion Models with a Dual-Backbone Approach","date":"2024-10-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"razvanmatisan/duodiff","path":"models/early_exit.py","file_url":"https://github.com/razvanmatisan/duodiff/blob/HEAD/models/early_exit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bd167b9d089c97b6","mcp_get_code":{"code_sha256":"bd167b9d089c97b6"}},{"arxiv_id":"2410.06264","paper":"/paper/think-while-you-generate-discrete-diffusion","title":"Think While You Generate: Discrete Diffusion with Planned Denoising","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liusulin/ddpd","path":"text8/model_planner.py","file_url":"https://github.com/liusulin/ddpd/blob/HEAD/text8/model_planner.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8762b0d2a4014cd","mcp_get_code":{"code_sha256":"c8762b0d2a4014cd"}},{"arxiv_id":"2410.01091","paper":"/paper/efficient-and-private-marginal-reconstruction","title":"Efficient and Private Marginal Reconstruction with Local Non-Negativity","date":"2024-10-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bcmullins/efficient-marginal-reconstruction","path":"src/rem/reconstruction.py","file_url":"https://github.com/bcmullins/efficient-marginal-reconstruction/blob/HEAD/src/rem/reconstruction.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d44f4c9d305682c9","mcp_get_code":{"code_sha256":"d44f4c9d305682c9"}},{"arxiv_id":"2409.11401","paper":"/paper/teaching-dark-matter-simulations-to-speak-the","title":"Teaching dark matter simulations to speak the halo language","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"shivampcosmo/gotham","path":"src/model_enc_dec.py","file_url":"https://github.com/shivampcosmo/gotham/blob/HEAD/src/model_enc_dec.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"316d8bef327bd229","mcp_get_code":{"code_sha256":"316d8bef327bd229"}},{"arxiv_id":"2409.10473","paper":"/paper/macdiff-unified-skeleton-modeling-with-masked","title":"MacDiff: Unified Skeleton Modeling with Masked Conditional Diffusion","date":"2024-09-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LehongWu/MacDiff","path":"model/transformer_macdiff.py","file_url":"https://github.com/LehongWu/MacDiff/blob/HEAD/model/transformer_macdiff.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d87bd702280998e1","mcp_get_code":{"code_sha256":"d87bd702280998e1"}},{"arxiv_id":"2409.09016","paper":"/paper/closed-loop-visuomotor-control-with","title":"Closed-Loop Visuomotor Control with Generative Expectation for Robotic Manipulation","date":null,"month_inferred_from_arxiv_id":"2024-09","title_source":"archive","repo":"OpenDriveLab/CLOVER","path":"FeedbackPolicy/models/policy.py","file_url":"https://github.com/OpenDriveLab/CLOVER/blob/HEAD/FeedbackPolicy/models/policy.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"92c6e1689d974314","mcp_get_code":{"code_sha256":"92c6e1689d974314"}},{"arxiv_id":"2407.16171","paper":"/paper/learning-trimodal-relation-for-avqa-with","title":"Learning Trimodal Relation for AVQA with Missing Modality","date":"2024-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisualAIKHU/Missing-AVQA","path":"net_grd_avst/net_avst.py","file_url":"https://github.com/VisualAIKHU/Missing-AVQA/blob/HEAD/net_grd_avst/net_avst.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1b1fcc6ab25fa4ef","mcp_get_code":{"code_sha256":"1b1fcc6ab25fa4ef"}},{"arxiv_id":"2407.15837","paper":"/paper/towards-latent-masked-image-modeling-for-self","title":"Towards Latent Masked Image Modeling for Self-Supervised Visual Representation Learning","date":"2024-07-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yibingwei-1/LatentMIM","path":"models_lmim.py","file_url":"https://github.com/yibingwei-1/LatentMIM/blob/HEAD/models_lmim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"88d3a859bbb353fa","mcp_get_code":{"code_sha256":"88d3a859bbb353fa"}},{"arxiv_id":"2407.11588","paper":"/paper/progressive-pretext-task-learning-for-human","title":"Progressive Pretext Task Learning for Human Trajectory Prediction","date":"2024-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"iSEE-Laboratory/PPT","path":"models/model.py","file_url":"https://github.com/iSEE-Laboratory/PPT/blob/HEAD/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d166e7adbdb2f7d0","mcp_get_code":{"code_sha256":"d166e7adbdb2f7d0"}},{"arxiv_id":"2406.09997","paper":"/paper/towards-scalable-and-versatile-weight-space","title":"Towards Scalable and Versatile Weight Space Learning","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hsg-aiml/sane","path":"src/SANE/models/def_AE.py","file_url":"https://github.com/hsg-aiml/sane/blob/HEAD/src/SANE/models/def_AE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e3b41e6df5019382","mcp_get_code":{"code_sha256":"e3b41e6df5019382"}},{"arxiv_id":"2406.05629","paper":"/paper/separating-the-chirp-from-the-chat-self","title":"Separating the \"Chirp\" from the \"Chat\": Self-supervised Visual Grounding of Sound and Language","date":"2024-06-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mhamilton723/DenseAV","path":"denseav/aligners.py","file_url":"https://github.com/mhamilton723/DenseAV/blob/HEAD/denseav/aligners.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"45cc57e3e92a308b","mcp_get_code":{"code_sha256":"45cc57e3e92a308b"}},{"arxiv_id":"2406.05478","paper":"/paper/revisiting-non-autoregressive-transformers","title":"Revisiting Non-Autoregressive Transformers for Efficient Image Synthesis","date":"2024-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/ImprovedNAT","path":"libs/nat_model.py","file_url":"https://github.com/LeapLabTHU/ImprovedNAT/blob/HEAD/libs/nat_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"edd136332f6ae67e","mcp_get_code":{"code_sha256":"edd136332f6ae67e"}},{"arxiv_id":"2406.01210","paper":"/paper/geminifusion-efficient-pixel-wise-multimodal","title":"GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision Transformer","date":"2024-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiadingcn/geminifusion","path":"models/mix_transformer.py","file_url":"https://github.com/jiadingcn/geminifusion/blob/HEAD/models/mix_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddc545386a46bb98","mcp_get_code":{"code_sha256":"ddc545386a46bb98"}},{"arxiv_id":"2405.20666","paper":"/paper/masa-motion-aware-masked-autoencoder-with","title":"MASA: Motion-aware Masked Autoencoder with Semantic Alignment for Sign Language Recognition","date":"2024-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sakura2233565548/masa","path":"moco/GCN_Transformer_mask.py","file_url":"https://github.com/sakura2233565548/masa/blob/HEAD/moco/GCN_Transformer_mask.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0bd39692ab2abbaa","mcp_get_code":{"code_sha256":"0bd39692ab2abbaa"}},{"arxiv_id":"2405.19783","paper":"/paper/instruction-guided-visual-masking","title":"Instruction-Guided Visual Masking","date":"2024-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"2toinf/ivm","path":"model/IVM.py","file_url":"https://github.com/2toinf/ivm/blob/HEAD/model/IVM.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2dcf225b97d045ae","mcp_get_code":{"code_sha256":"2dcf225b97d045ae"}},{"arxiv_id":"2405.16419","paper":"/paper/enhancing-feature-diversity-boosts-channel","title":"Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers","date":"2024-05-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chaudatascience/diverse_channel_vit","path":"models/dichavit.py","file_url":"https://github.com/chaudatascience/diverse_channel_vit/blob/HEAD/models/dichavit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"efa81f6f1800c5f2","mcp_get_code":{"code_sha256":"efa81f6f1800c5f2"}},{"arxiv_id":"2405.14791","paper":"/paper/recurrent-early-exits-for-federated-learning","title":"Recurrent Early Exits for Federated Learning with Heterogeneous Clients","date":"2024-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"royson/reefl","path":"src/models/reefl_vit.py","file_url":"https://github.com/royson/reefl/blob/HEAD/src/models/reefl_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b106d531af0900bc","mcp_get_code":{"code_sha256":"b106d531af0900bc"}},{"arxiv_id":"2405.13985","paper":"/paper/lookhere-vision-transformers-with-directed","title":"LookHere: Vision Transformers with Directed Attention Generalize and Extrapolate","date":"2024-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"greencubic/lookhere","path":"lookhere.py","file_url":"https://github.com/greencubic/lookhere/blob/HEAD/lookhere.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01c98d386037421d","mcp_get_code":{"code_sha256":"01c98d386037421d"}},{"arxiv_id":"2405.08740","paper":"/paper/reinformer-max-return-sequence-modeling-for","title":"Reinformer: Max-Return Sequence Modeling for Offline RL","date":"2024-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dragon-zhuang/reinformer","path":"model/reinformer.py","file_url":"https://github.com/dragon-zhuang/reinformer/blob/HEAD/model/reinformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"85eec4236310f344","mcp_get_code":{"code_sha256":"85eec4236310f344"}},{"arxiv_id":"2404.15700","paper":"/paper/mas-sam-segment-any-marine-animal-with","title":"MAS-SAM: Segment Any Marine Animal with Aggregated Features","date":"2024-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Drchip61/MAS-SAM","path":"MAS-SAM/sam_lora_image_encoder.py","file_url":"https://github.com/Drchip61/MAS-SAM/blob/HEAD/MAS-SAM/sam_lora_image_encoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8f4c88e2ba7c6212","mcp_get_code":{"code_sha256":"8f4c88e2ba7c6212"}},{"arxiv_id":"2404.12467","paper":"/paper/towards-multi-modal-transformers-in-federated","title":"Towards Multi-modal Transformers in Federated Learning","date":"2024-04-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"imguangyu/FedCola","path":"src/models/mome.py","file_url":"https://github.com/imguangyu/FedCola/blob/HEAD/src/models/mome.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"8a67e5c4a6bc779d","mcp_get_code":{"code_sha256":"8a67e5c4a6bc779d"}},{"arxiv_id":"2404.04624","paper":"/paper/bridging-the-gap-between-end-to-end-and-two","title":"Bridging the Gap Between End-to-End and Two-Step Text Spotting","date":"2024-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mxin262/bridging-text-spotting","path":"adet/modeling/bridge.py","file_url":"https://github.com/mxin262/bridging-text-spotting/blob/HEAD/adet/modeling/bridge.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5ad9f4a5115fc2c9","mcp_get_code":{"code_sha256":"5ad9f4a5115fc2c9"}},{"arxiv_id":"2404.00228","paper":"/paper/inflora-interference-free-low-rank-adaptation","title":"InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning","date":"2024-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangyanshuo/InfLoRA","path":"methods/inflora.py","file_url":"https://github.com/liangyanshuo/InfLoRA/blob/HEAD/methods/inflora.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"47dda0c70839da58","mcp_get_code":{"code_sha256":"47dda0c70839da58"}},{"arxiv_id":"2403.19967","paper":"/paper/rewrite-the-stars","title":"Rewrite the Stars","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ma-xu/Rewrite-the-Stars","path":"imagenet/starnet.py","file_url":"https://github.com/ma-xu/Rewrite-the-Stars/blob/HEAD/imagenet/starnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a47150747aa6cfcb","mcp_get_code":{"code_sha256":"a47150747aa6cfcb"}},{"arxiv_id":"2403.19638","paper":"/paper/siamese-vision-transformers-are-scalable","title":"Siamese Vision Transformers are Scalable Audio-visual Learners","date":"2024-03-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"GenjiB/AVSiam","path":"src/models/cav_mae_base.py","file_url":"https://github.com/GenjiB/AVSiam/blob/HEAD/src/models/cav_mae_base.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"534488ebed89ff98","mcp_get_code":{"code_sha256":"534488ebed89ff98"}},{"arxiv_id":"2403.17749","paper":"/paper/multi-task-dense-prediction-via-mixture-of","title":"Multi-Task Dense Prediction via Mixture of Low-Rank Experts","date":"2024-03-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuqiYang213/MLoRE","path":"models/transformers/MLoRE.py","file_url":"https://github.com/YuqiYang213/MLoRE/blob/HEAD/models/transformers/MLoRE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"39e1e82bab3c6883","mcp_get_code":{"code_sha256":"39e1e82bab3c6883"}},{"arxiv_id":"2403.15139","paper":"/paper/deep-generative-model-based-rate-distortion","title":"Deep Generative Model based Rate-Distortion for Image Downscaling Assessment","date":"2024-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IIGROUP/MANIQA","path":"models/maniqa.py","file_url":"https://github.com/IIGROUP/MANIQA/blob/HEAD/models/maniqa.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2746ad51b2733503","mcp_get_code":{"code_sha256":"2746ad51b2733503"}},{"arxiv_id":"2403.14715","paper":"/paper/understanding-why-label-smoothing-degrades","title":"Towards Understanding Why Label Smoothing Degrades Selective Classification and How to Fix It","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/deit","path":"models_v2.py","file_url":"https://github.com/facebookresearch/deit/blob/HEAD/models_v2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b02d29318271a1fa","mcp_get_code":{"code_sha256":"b02d29318271a1fa"}},{"arxiv_id":"2403.14198","paper":"/paper/unleashing-unlabeled-data-a-paradigm-for","title":"Unleashing Unlabeled Data: A Paradigm for Cross-View Geo-Localization","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liguopeng0923/UCVGL","path":"train_crossview_sat.py","file_url":"https://github.com/liguopeng0923/UCVGL/blob/HEAD/train_crossview_sat.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79f5b6c2523b2374","mcp_get_code":{"code_sha256":"79f5b6c2523b2374"}},{"arxiv_id":"2403.10254","paper":"/paper/magic-tokens-select-diverse-tokens-for-multi","title":"Magic Tokens: Select Diverse Tokens for Multi-modal Object Re-Identification","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"924973292/fusionreid","path":"modeling/fusion_part/fusion.py","file_url":"https://github.com/924973292/fusionreid/blob/HEAD/modeling/fusion_part/fusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cd31ba10bfe67622","mcp_get_code":{"code_sha256":"cd31ba10bfe67622"}},{"arxiv_id":"2403.09502","paper":"/paper/equiav-leveraging-equivariance-for-audio","title":"EquiAV: Leveraging Equivariance for Audio-Visual Contrastive Learning","date":"2024-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jongsuk1/equiav","path":"models/pt_EquiAV.py","file_url":"https://github.com/jongsuk1/equiav/blob/HEAD/models/pt_EquiAV.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"76fafb76150402b2","mcp_get_code":{"code_sha256":"76fafb76150402b2"}},{"arxiv_id":"2403.08568","paper":"/paper/consistent-prompting-for-rehearsal-free","title":"Consistent Prompting for Rehearsal-Free Continual Learning","date":"2024-03-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Zhanxin-Gao/CPrompt","path":"models/cprompt.py","file_url":"https://github.com/Zhanxin-Gao/CPrompt/blob/HEAD/models/cprompt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ca2b19634b227b1e","mcp_get_code":{"code_sha256":"ca2b19634b227b1e"}},{"arxiv_id":"2403.06977","paper":"/paper/videomamba-state-space-model-for-efficient","title":"VideoMamba: State Space Model for Efficient Video Understanding","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opengvlab/videomamba","path":"videomamba/video_sm/models/videomamba.py","file_url":"https://github.com/opengvlab/videomamba/blob/HEAD/videomamba/video_sm/models/videomamba.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f656db702b49f748","mcp_get_code":{"code_sha256":"f656db702b49f748"}},{"arxiv_id":"2403.05406","paper":"/paper/considering-nonstationary-within-multivariate","title":"Considering Nonstationary within Multivariate Time Series with Variational Hierarchical Transformer for Forecasting","date":"2024-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"flare200020/HTV_Trans","path":"HTV-Trans/models.py","file_url":"https://github.com/flare200020/HTV_Trans/blob/HEAD/HTV-Trans/models.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8783aa1a77c47581","mcp_get_code":{"code_sha256":"8783aa1a77c47581"}},{"arxiv_id":"2403.04492","paper":"/paper/discriminative-sample-guided-and-parameter","title":"Discriminative Sample-Guided and Parameter-Efficient Feature Space Adaptation for Cross-Domain Few-Shot Learning","date":"2024-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rashindrie/DIPA","path":"models/vision_transformer_extended.py","file_url":"https://github.com/rashindrie/DIPA/blob/HEAD/models/vision_transformer_extended.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"045a9bd3f28b8448","mcp_get_code":{"code_sha256":"045a9bd3f28b8448"}},{"arxiv_id":"2403.03542","paper":"/paper/dpot-auto-regressive-denoising-operator","title":"DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-Training","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HaoZhongkai/DPOT","path":"models/dpot.py","file_url":"https://github.com/HaoZhongkai/DPOT/blob/HEAD/models/dpot.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"994b3f1e743c60f0","mcp_get_code":{"code_sha256":"994b3f1e743c60f0"}},{"arxiv_id":"2403.01412","paper":"/paper/lum-vit-learnable-under-sampling-mask-vision","title":"LUM-ViT: Learnable Under-sampling Mask Vision Transformer for Bandwidth Limited Optical Signal Acquisition","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"maxllf/lum-vit","path":"LUM-ViT.py","file_url":"https://github.com/maxllf/lum-vit/blob/HEAD/LUM-ViT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"552fc396d62c3313","mcp_get_code":{"code_sha256":"552fc396d62c3313"}},{"arxiv_id":"2403.00939","paper":"/paper/g3dr-generative-3d-reconstruction-in-imagenet","title":"G3DR: Generative 3D Reconstruction in ImageNet","date":"2024-03-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"preddy5/G3DR","path":"src/unet.py","file_url":"https://github.com/preddy5/G3DR/blob/HEAD/src/unet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ca1f424da1d12329","mcp_get_code":{"code_sha256":"ca1f424da1d12329"}},{"arxiv_id":"2402.19009","paper":"/paper/generating-reconstructing-and-representing","title":"Unified Generation, Reconstruction, and Representation: Generalized Diffusion with Adaptive Latent Encoding-Decoding","date":"2024-02-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guangyliu/eddpm","path":"Protein/codes/nn/models.py","file_url":"https://github.com/guangyliu/eddpm/blob/HEAD/Protein/codes/nn/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6f0bd13ce861a0c","mcp_get_code":{"code_sha256":"d6f0bd13ce861a0c"}},{"arxiv_id":"2402.09378","paper":"/paper/mobilespeech-a-fast-and-high-fidelity","title":"MobileSpeech: A Fast and High-Fidelity Framework for Mobile Zero-Shot Text-to-Speech","date":"2024-02-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"enhuiz/vall-e","path":"vall_e/vall_e/ar.py","file_url":"https://github.com/enhuiz/vall-e/blob/HEAD/vall_e/vall_e/ar.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4d47845809fd8661","mcp_get_code":{"code_sha256":"4d47845809fd8661"}},{"arxiv_id":"2402.04754","paper":"/paper/towards-aligned-layout-generation-via","title":"Towards Aligned Layout Generation via Diffusion Model with Aesthetic Constraints","date":"2024-02-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"puar-playground/lace","path":"model_diffusion.py","file_url":"https://github.com/puar-playground/lace/blob/HEAD/model_diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33602d665cafbd1a","mcp_get_code":{"code_sha256":"33602d665cafbd1a"}},{"arxiv_id":"2402.00033","paper":"/paper/lf-vit-reducing-spatial-redundancy-in-vision","title":"LF-ViT: Reducing Spatial Redundancy in Vision Transformer for Efficient Image Recognition","date":"2024-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"edgeai1/LF-ViT","path":"deit/models_deit.py","file_url":"https://github.com/edgeai1/LF-ViT/blob/HEAD/deit/models_deit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a1b6bdfb470d9562","mcp_get_code":{"code_sha256":"a1b6bdfb470d9562"}},{"arxiv_id":"2401.15652","paper":"/paper/continuous-multiple-image-outpainting-in-one","title":"Continuous-Multiple Image Outpainting in One-Step via Positional Query and A Diffusion-based Approach","date":"2024-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sherrylone/pqdiff","path":"models/uvit.py","file_url":"https://github.com/sherrylone/pqdiff/blob/HEAD/models/uvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0d0f37bb8cc3f1c3","mcp_get_code":{"code_sha256":"0d0f37bb8cc3f1c3"}},{"arxiv_id":"2401.08083","paper":"/paper/uv-sam-adapting-segment-anything-model-for","title":"UV-SAM: Adapting Segment Anything Model for Urban Village Identification","date":"2024-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsinghua-fib-lab/uv-sam","path":"modules/sam/modeling/sam.py","file_url":"https://github.com/tsinghua-fib-lab/uv-sam/blob/HEAD/modules/sam/modeling/sam.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d841a9adef572f9e","mcp_get_code":{"code_sha256":"d841a9adef572f9e"}},{"arxiv_id":"2401.06155","paper":"/paper/de-novo-drug-design-using-reinforcement-1","title":"De novo Drug Design using Reinforcement Learning with Multiple GPT Agents","date":"2023-12-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hxyfighter/molrl-mgpt","path":"codes/model.py","file_url":"https://github.com/hxyfighter/molrl-mgpt/blob/HEAD/codes/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"526e085e16e15412","mcp_get_code":{"code_sha256":"526e085e16e15412"}},{"arxiv_id":"2401.00254","paper":"/paper/masked-image-modeling-via-dynamic-token","title":"Morphing Tokens Draw Strong Masked Image Models","date":"2023-12-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver-ai/dtm","path":"models/modeling_dtm.py","file_url":"https://github.com/naver-ai/dtm/blob/HEAD/models/modeling_dtm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"20a4244244f3e48e","mcp_get_code":{"code_sha256":"20a4244244f3e48e"}},{"arxiv_id":"2312.12619","paper":"/paper/hierarchical-vision-transformers-for-context","title":"Hierarchical Vision Transformers for Context-Aware Prostate Cancer Grading in Whole Slide Images","date":"2023-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"computationalpathologygroup/hvit","path":"source/models.py","file_url":"https://github.com/computationalpathologygroup/hvit/blob/HEAD/source/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"83d4c1f6303ecaa4","mcp_get_code":{"code_sha256":"83d4c1f6303ecaa4"}},{"arxiv_id":"2312.06647","paper":"/paper/4m-massively-multimodal-masked-modeling-1","title":"4M: Massively Multimodal Masked Modeling","date":"2023-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apple/ml-4m","path":"fourm/models/fm.py","file_url":"https://github.com/apple/ml-4m/blob/HEAD/fourm/models/fm.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"66f58c21558cf0b3","mcp_get_code":{"code_sha256":"66f58c21558cf0b3"}},{"arxiv_id":"2312.03701","paper":"/paper/self-conditioned-image-generation-via","title":"Return of Unconditional Generation: A Self-supervised Representation Generation Method","date":"2023-12-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LTH14/rcg","path":"pixel_generator/mage/models_mage.py","file_url":"https://github.com/LTH14/rcg/blob/HEAD/pixel_generator/mage/models_mage.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"607e78af8a011f8d","mcp_get_code":{"code_sha256":"607e78af8a011f8d"}},{"arxiv_id":"2311.18825","paper":"/paper/cast-cross-attention-in-space-and-time-for-1","title":"CAST: Cross-Attention in Space and Time for Video Action Recognition","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"khu-vll/cast","path":"models/bidir_modeling_crossattn.py","file_url":"https://github.com/khu-vll/cast/blob/HEAD/models/bidir_modeling_crossattn.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"1dd28bf10048de7d","mcp_get_code":{"code_sha256":"1dd28bf10048de7d"}},{"arxiv_id":"2311.06231","paper":"/paper/learning-human-action-recognition","title":"Learning Human Action Recognition Representations Without Real Humans","date":"2023-11-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"howardzh01/ppma","path":"code/omnivision/models/vision_transformer.py","file_url":"https://github.com/howardzh01/ppma/blob/HEAD/code/omnivision/models/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"eecc5dc23f84ce7c","mcp_get_code":{"code_sha256":"eecc5dc23f84ce7c"}},{"arxiv_id":"2310.13545","paper":"/paper/scalelong-towards-more-stable-training-of-1","title":"ScaleLong: Towards More Stable Training of Diffusion Model via Scaling Network Long Skip Connection","date":"2023-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/scalelong","path":"libs/uvit.py","file_url":"https://github.com/sail-sg/scalelong/blob/HEAD/libs/uvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b4ecd978b7e2b6d9","mcp_get_code":{"code_sha256":"b4ecd978b7e2b6d9"}},{"arxiv_id":"2310.04948","paper":"/paper/tempo-prompt-based-generative-pre-trained","title":"TEMPO: Prompt-based Generative Pre-trained Transformer for Time Series Forecasting","date":"2023-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liaoyuhua/tempo-pytorch","path":"src/model.py","file_url":"https://github.com/liaoyuhua/tempo-pytorch/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"becc71810b11d8de","mcp_get_code":{"code_sha256":"becc71810b11d8de"}},{"arxiv_id":"2309.07207","paper":"/paper/earthpt-a-foundation-model-for-earth","title":"EarthPT: a time series foundation model for Earth Observation","date":"2023-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"aspiaspace/earthpt","path":"src/model.py","file_url":"https://github.com/aspiaspace/earthpt/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7589e0b66c2f55b","mcp_get_code":{"code_sha256":"b7589e0b66c2f55b"}},{"arxiv_id":"2309.03729","paper":"/paper/phasic-content-fusing-diffusion-model-with","title":"Phasic Content Fusing Diffusion Model with Directional Distribution Consistency for Few-Shot Model Adaption","date":"2023-09-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sjtuplayer/few-shot-diffusion","path":"model/big_unet.py","file_url":"https://github.com/sjtuplayer/few-shot-diffusion/blob/HEAD/model/big_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d16665e1f56437fb","mcp_get_code":{"code_sha256":"d16665e1f56437fb"}},{"arxiv_id":"2308.15512","paper":"/paper/shatter-and-gather-learning-referring-image","title":"Shatter and Gather: Learning Referring Image Segmentation with Text Supervision","date":"2023-08-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdwonn/SaG","path":"model/cross_modal_attention.py","file_url":"https://github.com/kdwonn/SaG/blob/HEAD/model/cross_modal_attention.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"da0442761fe33eed","mcp_get_code":{"code_sha256":"da0442761fe33eed"}},{"arxiv_id":"2308.13494","paper":"/paper/eventful-transformers-leveraging-temporal","title":"Eventful Transformers: Leveraging Temporal Redundancy in Vision Transformers","date":"2023-08-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WISION-Lab/eventful-transformer","path":"eventful_transformer/blocks.py","file_url":"https://github.com/WISION-Lab/eventful-transformer/blob/HEAD/eventful_transformer/blocks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bf0f9ab39bcc2adc","mcp_get_code":{"code_sha256":"bf0f9ab39bcc2adc"}},{"arxiv_id":"2308.12216","paper":"/paper/sg-former-self-guided-transformer-with","title":"SG-Former: Self-guided Transformer with Evolving Token Reallocation","date":"2023-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OliverRensu/SG-Former","path":"sgformer.py","file_url":"https://github.com/OliverRensu/SG-Former/blob/HEAD/sgformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"217841cd4c280b17","mcp_get_code":{"code_sha256":"217841cd4c280b17"}},{"arxiv_id":"2308.09951","paper":"/paper/semantics-meets-temporal-correspondence-self","title":"Semantics Meets Temporal Correspondence: Self-supervised Object-centric Learning in Videos","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shvdiwnkozbw/SMTC","path":"src/model/model_action.py","file_url":"https://github.com/shvdiwnkozbw/SMTC/blob/HEAD/src/model/model_action.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fdfb57b897e2108a","mcp_get_code":{"code_sha256":"fdfb57b897e2108a"}},{"arxiv_id":"2308.04808","paper":"/paper/joint-relation-transformer-for-multi-person","title":"Joint-Relation Transformer for Multi-Person Motion Prediction","date":"2023-08-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/JRTransformer","path":"model/model.py","file_url":"https://github.com/MediaBrain-SJTU/JRTransformer/blob/HEAD/model/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7f79674712326709","mcp_get_code":{"code_sha256":"7f79674712326709"}},{"arxiv_id":"2308.04549","paper":"/paper/prune-spatio-temporal-tokens-by-semantic","title":"Prune Spatio-temporal Tokens by Semantic-aware Temporal Accumulation","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mark12Ding/STA","path":"model_vit.py","file_url":"https://github.com/Mark12Ding/STA/blob/HEAD/model_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"981e00fd80e33c21","mcp_get_code":{"code_sha256":"981e00fd80e33c21"}},{"arxiv_id":"2307.15324","paper":"/paper/taskexpert-dynamically-assembling-multi-task","title":"TaskExpert: Dynamically Assembling Multi-Task Representations with Memorial Mixture-of-Experts","date":"2023-07-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prismformore/multi-task-transformer","path":"TaskPrompter/models/transformers/taskprompter.py","file_url":"https://github.com/prismformore/multi-task-transformer/blob/HEAD/TaskPrompter/models/transformers/taskprompter.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"786d3dd6c884f03f","mcp_get_code":{"code_sha256":"786d3dd6c884f03f"}},{"arxiv_id":"2307.14008","paper":"/paper/adaptive-frequency-filters-as-efficient","title":"Adaptive Frequency Filters As Efficient Global Token Mixers","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"NWPU-Li/AFFNet","path":"aff_block_LL.py","file_url":"https://github.com/NWPU-Li/AFFNet/blob/HEAD/aff_block_LL.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f391210c1cd38e6c","mcp_get_code":{"code_sha256":"f391210c1cd38e6c"}},{"arxiv_id":"2307.08579","paper":"/paper/scale-aware-modulation-meet-transformer","title":"Scale-Aware Modulation Meet Transformer","date":"2023-07-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AFeng-x/SMT","path":"models/smt.py","file_url":"https://github.com/AFeng-x/SMT/blob/HEAD/models/smt.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9d6bd0fe4a730f0d","mcp_get_code":{"code_sha256":"9d6bd0fe4a730f0d"}},{"arxiv_id":"2307.04895","paper":"/paper/learning-to-solve-constraint-satisfaction","title":"Learning to Solve Constraint Satisfaction Problems with Recurrent Transformer","date":"2023-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"azreasoners/recurrent_transformer","path":"mingpt/model.py","file_url":"https://github.com/azreasoners/recurrent_transformer/blob/HEAD/mingpt/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d5a04fb7481be04b","mcp_get_code":{"code_sha256":"d5a04fb7481be04b"}},{"arxiv_id":"2306.15794","paper":"/paper/hyenadna-long-range-genomic-sequence-modeling","title":"HyenaDNA: Long-Range Genomic Sequence Modeling at Single Nucleotide Resolution","date":"2023-06-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"frederikkemarin/bend","path":"bend/models/hyena_dna.py","file_url":"https://github.com/frederikkemarin/bend/blob/HEAD/bend/models/hyena_dna.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"15e87d04566e903e","mcp_get_code":{"code_sha256":"15e87d04566e903e"}},{"arxiv_id":"2306.07957","paper":"/paper/hidden-biases-of-end-to-end-driving-models","title":"Hidden Biases of End-to-End Driving Models","date":"2023-06-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/carla_garage","path":"team_code/transfuser.py","file_url":"https://github.com/autonomousvision/carla_garage/blob/HEAD/team_code/transfuser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"80dbac57b0bfe077","mcp_get_code":{"code_sha256":"80dbac57b0bfe077"}},{"arxiv_id":"2306.05067","paper":"/paper/improving-visual-prompt-tuning-for-self","title":"Improving Visual Prompt Tuning for Self-supervised Vision Transformers","date":"2023-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryongithub/gatedprompttuning","path":"src/models/vit_prompt/vit_mae.py","file_url":"https://github.com/ryongithub/gatedprompttuning/blob/HEAD/src/models/vit_prompt/vit_mae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5667ba706eacf9a5","mcp_get_code":{"code_sha256":"5667ba706eacf9a5"}},{"arxiv_id":"2305.08457","paper":"/paper/molhf-a-hierarchical-normalizing-flow-for","title":"MolHF: A Hierarchical Normalizing Flow for Molecular Graph Generation","date":"2023-05-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"violet-sto/molhf","path":"models/MolHF.py","file_url":"https://github.com/violet-sto/molhf/blob/HEAD/models/MolHF.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"46b1f7a69cd6719a","mcp_get_code":{"code_sha256":"46b1f7a69cd6719a"}},{"arxiv_id":"2305.04073","paper":"/paper/explaining-rl-decisions-with-trajectories","title":"Explaining RL Decisions with Trajectories","date":"2023-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karim-abdel/fact","path":"Breakout/mk_patch_decision_transformer_atari.py","file_url":"https://github.com/karim-abdel/fact/blob/HEAD/Breakout/mk_patch_decision_transformer_atari.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0bf497e52b9795d0","mcp_get_code":{"code_sha256":"0bf497e52b9795d0"}},{"arxiv_id":"2304.07221","paper":"/paper/instance-aware-dynamic-prompt-tuning-for-pre","title":"Instance-aware Dynamic Prompt Tuning for Pre-trained Point Cloud Models","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zyh16143998882/IDPT","path":"models/Point_MAE.py","file_url":"https://github.com/zyh16143998882/IDPT/blob/HEAD/models/Point_MAE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e50a82843946d9b4","mcp_get_code":{"code_sha256":"e50a82843946d9b4"}},{"arxiv_id":"2304.07193","paper":"/paper/dinov2-learning-robust-visual-features","title":"DINOv2: Learning Robust Visual Features without Supervision","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ByungKwanLee/Causal-Unsupervised-Segmentation","path":"models/dinov2vit.py","file_url":"https://github.com/ByungKwanLee/Causal-Unsupervised-Segmentation/blob/HEAD/models/dinov2vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0d03b714971c0501","mcp_get_code":{"code_sha256":"0d03b714971c0501"}},{"arxiv_id":"2304.07193","paper":"/paper/dinov2-learning-robust-visual-features","title":"DINOv2: Learning Robust Visual Features without Supervision","date":"2023-04-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/highrescanopyheight","path":"models/backbone.py","file_url":"https://github.com/facebookresearch/highrescanopyheight/blob/HEAD/models/backbone.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"912e7d3558003565","mcp_get_code":{"code_sha256":"912e7d3558003565"}},{"arxiv_id":"2304.04952","paper":"/paper/data-efficient-image-quality-assessment-with","title":"Data-Efficient Image Quality Assessment with Attention-Panel Decoder","date":"2023-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"narthchin/DEIQT","path":"models/deiqt.py","file_url":"https://github.com/narthchin/DEIQT/blob/HEAD/models/deiqt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ebd3c4402fb09d09","mcp_get_code":{"code_sha256":"ebd3c4402fb09d09"}},{"arxiv_id":"2304.03283","paper":"/paper/diffusion-models-as-masked-autoencoders","title":"Diffusion Models as Masked Autoencoders","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kimdanni/DiffMAE","path":"models_cross.py","file_url":"https://github.com/kimdanni/DiffMAE/blob/HEAD/models_cross.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a766b080f0eacfd2","mcp_get_code":{"code_sha256":"a766b080f0eacfd2"}},{"arxiv_id":"2304.03195","paper":"/paper/micron-bert-bert-based-facial-micro","title":"Micron-BERT: BERT-based Facial Micro-Expression Recognition","date":"2023-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uark-cviu/Micron-BERT","path":"models/vision_transformer.py","file_url":"https://github.com/uark-cviu/Micron-BERT/blob/HEAD/models/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fc0412e634ad5a9d","mcp_get_code":{"code_sha256":"fc0412e634ad5a9d"}},{"arxiv_id":"2304.00776","paper":"/paper/chain-of-thought-predictive-control","title":"Chain-of-Thought Predictive Control","date":"2023-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"seanjia/cotpc","path":"src/model.py","file_url":"https://github.com/seanjia/cotpc/blob/HEAD/src/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ab818ce189993218","mcp_get_code":{"code_sha256":"ab818ce189993218"}},{"arxiv_id":"2303.17472","paper":"/paper/poseformerv2-exploring-frequency-domain-for","title":"PoseFormerV2: Exploring Frequency Domain for Efficient and Robust 3D Human Pose Estimation","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"qitaozhao/poseformerv2","path":"mpi_inf_3dhp/model/model_poseformerv2.py","file_url":"https://github.com/qitaozhao/poseformerv2/blob/HEAD/mpi_inf_3dhp/model/model_poseformerv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3bc3259dcec516c","mcp_get_code":{"code_sha256":"b3bc3259dcec516c"}},{"arxiv_id":"2303.17152","paper":"/paper/mixed-autoencoder-for-self-supervised-visual","title":"Mixed Autoencoder for Self-supervised Visual Representation Learning","date":"2023-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Natyren/MixedAE","path":"mixedae/mixedae.py","file_url":"https://github.com/Natyren/MixedAE/blob/HEAD/mixedae/mixedae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62cd48dce0f3ccbb","mcp_get_code":{"code_sha256":"62cd48dce0f3ccbb"}},{"arxiv_id":"2303.16894","paper":"/paper/viewrefer-grasp-the-multi-view-knowledge-for","title":"ViewRefer: Grasp the Multi-view Knowledge for 3D Visual Grounding with GPT and Prototype Guidance","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ivan-tang-3d/point-peft","path":"MAE/models/Point_MAE_cp.py","file_url":"https://github.com/ivan-tang-3d/point-peft/blob/HEAD/MAE/models/Point_MAE_cp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ac34f59de765420a","mcp_get_code":{"code_sha256":"ac34f59de765420a"}},{"arxiv_id":"2303.16727","paper":"/paper/videomae-v2-scaling-video-masked-autoencoders","title":"VideoMAE V2: Scaling Video Masked Autoencoders with Dual Masking","date":"2023-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/VideoMAEv2","path":"models/modeling_pretrain.py","file_url":"https://github.com/OpenGVLab/VideoMAEv2/blob/HEAD/models/modeling_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ef3f731f47fa62cd","mcp_get_code":{"code_sha256":"ef3f731f47fa62cd"}},{"arxiv_id":"2303.15322","paper":"/paper/progressive-semantic-visual-mutual-adaption","title":"Progressive Semantic-Visual Mutual Adaption for Generalized Zero-Shot Learning","date":"2023-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ManLiuCoder/PSVMA","path":"models/modeling/PSVMAModel/PSVMANet.py","file_url":"https://github.com/ManLiuCoder/PSVMA/blob/HEAD/models/modeling/PSVMAModel/PSVMANet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a2572b7acd0772c5","mcp_get_code":{"code_sha256":"a2572b7acd0772c5"}},{"arxiv_id":"2303.14218","paper":"/paper/curricular-contrastive-regularization-for","title":"Curricular Contrastive Regularization for Physics-aware Single Image Dehazing","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YuZheng9/C2PNet","path":"models/C2PNet.py","file_url":"https://github.com/YuZheng9/C2PNet/blob/HEAD/models/C2PNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"afd1bac67081306e","mcp_get_code":{"code_sha256":"afd1bac67081306e"}},{"arxiv_id":"2303.13755","paper":"/paper/sparsifiner-learning-sparse-instance","title":"Sparsifiner: Learning Sparse Instance-Dependent Attention for Efficient Vision Transformers","date":"2023-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lim142857/Sparsifiner","path":"src/models/sparsifiner.py","file_url":"https://github.com/lim142857/Sparsifiner/blob/HEAD/src/models/sparsifiner.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b461976af475942","mcp_get_code":{"code_sha256":"6b461976af475942"}},{"arxiv_id":"2303.12670","paper":"/paper/correlational-image-modeling-for-self","title":"Correlational Image Modeling for Self-Supervised Visual Pre-Training","date":"2023-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weivision/correlational-image-modeling","path":"models/cim.py","file_url":"https://github.com/weivision/correlational-image-modeling/blob/HEAD/models/cim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c7a6f7abc4b8ea31","mcp_get_code":{"code_sha256":"c7a6f7abc4b8ea31"}},{"arxiv_id":"2303.12208","paper":"/paper/magvlt-masked-generative-vision-and-language","title":"MAGVLT: Masked Generative Vision-and-Language Transformer","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kakaobrain/magvlt","path":"magvlt/models/stage2/transformers.py","file_url":"https://github.com/kakaobrain/magvlt/blob/HEAD/magvlt/models/stage2/transformers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"89844ef2ac1027a2","mcp_get_code":{"code_sha256":"89844ef2ac1027a2"}},{"arxiv_id":"2303.11674","paper":"/paper/aloft-a-lightweight-mlp-like-architecture","title":"ALOFT: A Lightweight MLP-like Architecture with Dynamic Low-frequency Transform for Domain Generalization","date":"2023-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lingeringlight/ALOFT","path":"gfnet.py","file_url":"https://github.com/lingeringlight/ALOFT/blob/HEAD/gfnet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"426bd91e315bf4e9","mcp_get_code":{"code_sha256":"426bd91e315bf4e9"}},{"arxiv_id":"2303.10438","paper":"/paper/spatial-aware-token-for-weakly-supervised","title":"Spatial-Aware Token for Weakly Supervised Object Localization","date":"2023-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wpy1999/SAT","path":"Model/SAT.py","file_url":"https://github.com/wpy1999/SAT/blob/HEAD/Model/SAT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d824727765fcbcc6","mcp_get_code":{"code_sha256":"d824727765fcbcc6"}},{"arxiv_id":"2303.09663","paper":"/paper/efficient-computation-sharing-for-multi-task","title":"Efficient Computation Sharing for Multi-Task Visual Scene Understanding","date":"2023-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sarashoouri/EfficientMTL","path":"Codes/multimae/multimae.py","file_url":"https://github.com/sarashoouri/EfficientMTL/blob/HEAD/Codes/multimae/multimae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3cb7e68ce1e3f5f2","mcp_get_code":{"code_sha256":"3cb7e68ce1e3f5f2"}},{"arxiv_id":"2303.08331","paper":"/paper/towards-high-quality-and-efficient-video","title":"Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting","date":"2023-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"coulsonlee/STDO-CVPR2023","path":"models/wdsr.py","file_url":"https://github.com/coulsonlee/STDO-CVPR2023/blob/HEAD/models/wdsr.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c6c53d5c5ab3b1c7","mcp_get_code":{"code_sha256":"c6c53d5c5ab3b1c7"}},{"arxiv_id":"2303.08129","paper":"/paper/pimae-point-cloud-and-image-interactive","title":"PiMAE: Point Cloud and Image Interactive Masked Autoencoders for 3D Object Detection","date":"2023-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BLVLab/PiMAE","path":"Pretrain/models/pimae.py","file_url":"https://github.com/BLVLab/PiMAE/blob/HEAD/Pretrain/models/pimae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"fd6e69429d55cc68","mcp_get_code":{"code_sha256":"fd6e69429d55cc68"}},{"arxiv_id":"2303.08085","paper":"/paper/alias-free-convnets-fractional-shift","title":"Alias-Free Convnets: Fractional Shift Invariance via Polynomial Activations","date":"2023-03-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmichaeli/alias_free_convnets","path":"models/convnext_afc.py","file_url":"https://github.com/hmichaeli/alias_free_convnets/blob/HEAD/models/convnext_afc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"02099da423590637","mcp_get_code":{"code_sha256":"02099da423590637"}},{"arxiv_id":"2303.06457","paper":"/paper/active-visual-exploration-based-on-attention","title":"Active Visual Exploration Based on Attention-Map Entropy","date":"2023-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apardyl/AME","path":"architectures/selectors.py","file_url":"https://github.com/apardyl/AME/blob/HEAD/architectures/selectors.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d3c8bd812a7774f9","mcp_get_code":{"code_sha256":"d3c8bd812a7774f9"}},{"arxiv_id":"2303.05675","paper":"/paper/humanbench-towards-general-human-centric","title":"HumanBench: Towards General Human-centric Perception with Projector Assisted Pretraining","date":"2023-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OpenGVLab/HumanBench","path":"PATH/core/models/backbones/vitdet_for_ladder_attention_share_pos_embed.py","file_url":"https://github.com/OpenGVLab/HumanBench/blob/HEAD/PATH/core/models/backbones/vitdet_for_ladder_attention_share_pos_embed.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"344d0eb15f2cb514","mcp_get_code":{"code_sha256":"344d0eb15f2cb514"}},{"arxiv_id":"2303.04249","paper":"/paper/where-we-are-and-what-we-re-looking-at-query","title":"Where We Are and What We're Looking At: Query Based Worldwide Image Geo-localization Using Hierarchies and Scenes","date":"2023-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AHKerrigan/GeoGuessNet","path":"networks.py","file_url":"https://github.com/AHKerrigan/GeoGuessNet/blob/HEAD/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16488f5ead0b3722","mcp_get_code":{"code_sha256":"16488f5ead0b3722"}},{"arxiv_id":"2303.03391","paper":"/paper/decision-transformer-under-random-frame","title":"Decision Transformer under Random Frame Dropping","date":"2023-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hukz18/defog","path":"atari/model.py","file_url":"https://github.com/hukz18/defog/blob/HEAD/atari/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"803bee55ce19f9a2","mcp_get_code":{"code_sha256":"803bee55ce19f9a2"}},{"arxiv_id":"2302.13971","paper":"/paper/llama-open-and-efficient-foundation-language-1","title":"LLaMA: Open and Efficient Foundation Language Models","date":"2023-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Lightning-AI/lit-llama","path":"lit_llama/model.py","file_url":"https://github.com/Lightning-AI/lit-llama/blob/HEAD/lit_llama/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d24315a7d247960d","mcp_get_code":{"code_sha256":"d24315a7d247960d"}},{"arxiv_id":"2302.11939","paper":"/paper/power-time-series-forecasting-by-pretrained","title":"One Fits All:Power General Time Series Analysis by Pretrained LM","date":"2023-02-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liaoyuhua/GPT-TS","path":"src/model.py","file_url":"https://github.com/liaoyuhua/GPT-TS/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"283abdbdd3b3be9d","mcp_get_code":{"code_sha256":"283abdbdd3b3be9d"}},{"arxiv_id":"2302.01056","paper":"/paper/beyond-pretrained-features-noisy-image-1","title":"Beyond Pretrained Features: Noisy Image Modeling Provides Adversarial Defense","date":"2023-02-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"youzunzhi/nim-advdef","path":"model/mae/nim_mae.py","file_url":"https://github.com/youzunzhi/nim-advdef/blob/HEAD/model/mae/nim_mae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b67feaa0c67281dc","mcp_get_code":{"code_sha256":"b67feaa0c67281dc"}},{"arxiv_id":"2301.10222","paper":"/paper/rangevit-towards-vision-transformers-for-3d","title":"RangeViT: Towards Vision Transformers for 3D Semantic Segmentation in Autonomous Driving","date":"2023-01-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/rangevit","path":"models/rangevit.py","file_url":"https://github.com/valeoai/rangevit/blob/HEAD/models/rangevit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"a95957a1d20487a9","mcp_get_code":{"code_sha256":"a95957a1d20487a9"}},{"arxiv_id":"2301.08243","paper":"/paper/self-supervised-learning-from-images-with-a","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","date":"2023-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/ijepa","path":"src/models/vision_transformer.py","file_url":"https://github.com/facebookresearch/ijepa/blob/HEAD/src/models/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5a10f46fdda5375c","mcp_get_code":{"code_sha256":"5a10f46fdda5375c"}},{"arxiv_id":"2301.08243","paper":"/paper/self-supervised-learning-from-images-with-a","title":"Self-Supervised Learning from Images with a Joint-Embedding Predictive Architecture","date":"2023-01-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"waterdisappear/sar-jepa","path":"Pretraining/models_lomar.py","file_url":"https://github.com/waterdisappear/sar-jepa/blob/HEAD/Pretraining/models_lomar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"6710d315e3285047","mcp_get_code":{"code_sha256":"6710d315e3285047"}},{"arxiv_id":"2301.06052","paper":"/paper/t2m-gpt-generating-human-motion-from-textual","title":"T2M-GPT: Generating Human Motion from Textual Descriptions with Discrete Representations","date":"2023-01-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mael-zys/T2M-GPT","path":"models/t2m_trans.py","file_url":"https://github.com/Mael-zys/T2M-GPT/blob/HEAD/models/t2m_trans.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"c850a045304b395b","mcp_get_code":{"code_sha256":"c850a045304b395b"}},{"arxiv_id":"2301.01296","paper":"/paper/tinymim-an-empirical-study-of-distilling-mim","title":"TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models","date":"2023-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"oliverrensu/d-igpt","path":"DiGPT_torch/models_digpt.py","file_url":"https://github.com/oliverrensu/d-igpt/blob/HEAD/DiGPT_torch/models_digpt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3def779b7964615e","mcp_get_code":{"code_sha256":"3def779b7964615e"}},{"arxiv_id":"2301.01296","paper":"/paper/tinymim-an-empirical-study-of-distilling-mim","title":"TinyMIM: An Empirical Study of Distilling MIM Pre-trained Models","date":"2023-01-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OliverRensu/TinyMIM","path":"models_tinymim.py","file_url":"https://github.com/OliverRensu/TinyMIM/blob/HEAD/models_tinymim.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"01d6c22c6a63aed1","mcp_get_code":{"code_sha256":"01d6c22c6a63aed1"}},{"arxiv_id":"2301.00808","paper":"/paper/convnext-v2-co-designing-and-scaling-convnets","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vishalned/MMEarth-train","path":"models/convnextv2.py","file_url":"https://github.com/vishalned/MMEarth-train/blob/HEAD/models/convnextv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"2c6904dfd0e3ee2a","mcp_get_code":{"code_sha256":"2c6904dfd0e3ee2a"}},{"arxiv_id":"2301.00808","paper":"/paper/convnext-v2-co-designing-and-scaling-convnets","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zibbini/convnext-v2_tensorflow","path":"convnext_pt/convnextv2.py","file_url":"https://github.com/zibbini/convnext-v2_tensorflow/blob/HEAD/convnext_pt/convnextv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"af25ab15eb29c44b","mcp_get_code":{"code_sha256":"af25ab15eb29c44b"}},{"arxiv_id":"2301.00808","paper":"/paper/convnext-v2-co-designing-and-scaling-convnets","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jacky-Android/convnext-v2-pytorch","path":"model.py","file_url":"https://github.com/Jacky-Android/convnext-v2-pytorch/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f3c65efbbcd4b2a2","mcp_get_code":{"code_sha256":"f3c65efbbcd4b2a2"}},{"arxiv_id":"2301.00808","paper":"/paper/convnext-v2-co-designing-and-scaling-convnets","title":"ConvNeXt V2: Co-designing and Scaling ConvNets with Masked Autoencoders","date":"2023-01-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/convnext-v2","path":"models/convnextv2.py","file_url":"https://github.com/facebookresearch/convnext-v2/blob/HEAD/models/convnextv2.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"24e783957539d177","mcp_get_code":{"code_sha256":"24e783957539d177"}},{"arxiv_id":"2212.03465","paper":"/paper/mediar-harmony-of-data-centric-and-model","title":"MEDIAR: Harmony of Data-Centric and Model-Centric for Multi-Modality Microscopy","date":"2022-12-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"joonkeekim/mediar-napari","path":"segmentation_models_pytorch/encoders/mix_transformer.py","file_url":"https://github.com/joonkeekim/mediar-napari/blob/HEAD/segmentation_models_pytorch/encoders/mix_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"1e9303448e742eb5","mcp_get_code":{"code_sha256":"1e9303448e742eb5"}},{"arxiv_id":"2211.10636","paper":"/paper/efficient-video-representation-learning-via","title":"EVEREST: Efficient Masked Video Autoencoder by Removing Redundant Spatiotemporal Tokens","date":"2022-11-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunilhoho/everest","path":"modeling_pretrain.py","file_url":"https://github.com/sunilhoho/everest/blob/HEAD/modeling_pretrain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2fdd988be47b01b8","mcp_get_code":{"code_sha256":"2fdd988be47b01b8"}},{"arxiv_id":"2211.05187","paper":"/paper/training-a-vision-transformer-from-scratch-in","title":"Training a Vision Transformer from scratch in less than 24 hours with 1 GPU","date":"2022-11-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BorealisAI/efficient-vit-training","path":"models_vit/localvit.py","file_url":"https://github.com/BorealisAI/efficient-vit-training/blob/HEAD/models_vit/localvit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f4849c8a28f22c7b","mcp_get_code":{"code_sha256":"f4849c8a28f22c7b"}},{"arxiv_id":"2210.11016","paper":"/paper/towards-sustainable-self-supervised-learning","title":"Towards Sustainable Self-supervised Learning","date":"2022-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sail-sg/tec","path":"models/models_tec_vit.py","file_url":"https://github.com/sail-sg/tec/blob/HEAD/models/models_tec_vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"18847b8fadd70db0","mcp_get_code":{"code_sha256":"18847b8fadd70db0"}},{"arxiv_id":"2210.10716","paper":"/paper/croco-self-supervised-pre-training-for-3d","title":"CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion","date":"2022-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver/croco","path":"models/croco.py","file_url":"https://github.com/naver/croco/blob/HEAD/models/croco.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"d98a51333a14237d","mcp_get_code":{"code_sha256":"d98a51333a14237d"}},{"arxiv_id":"2209.14156","paper":"/paper/tvlt-textless-vision-language-transformer","title":"TVLT: Textless Vision-Language Transformer","date":"2022-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zinengtang/tvlt","path":"model/modules/tvlt.py","file_url":"https://github.com/zinengtang/tvlt/blob/HEAD/model/modules/tvlt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9e716f9035048a8a","mcp_get_code":{"code_sha256":"9e716f9035048a8a"}},{"arxiv_id":"2209.08956","paper":"/paper/panoramic-vision-transformer-for-saliency","title":"Panoramic Vision Transformer for Saliency Detection in 360° Videos","date":"2022-09-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hs-yn/paver","path":"code/model/decoder.py","file_url":"https://github.com/hs-yn/paver/blob/HEAD/code/model/decoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac7ba009078385e6","mcp_get_code":{"code_sha256":"ac7ba009078385e6"}},{"arxiv_id":"2209.06192","paper":"/paper/storydall-e-adapting-pretrained-text-to-image","title":"StoryDALL-E: Adapting Pretrained Text-to-Image Transformers for Story Continuation","date":"2022-09-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"adymaharana/storydalle","path":"story-dalle/dalle/models/stage2/transformer.py","file_url":"https://github.com/adymaharana/storydalle/blob/HEAD/story-dalle/dalle/models/stage2/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5f34dd7c6d84070b","mcp_get_code":{"code_sha256":"5f34dd7c6d84070b"}},{"arxiv_id":"2209.01404","paper":"/paper/towards-accurate-binary-neural-networks-via","title":"Towards Accurate Binary Neural Networks via Modeling Contextual Dependencies","date":"2022-09-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Sense-GVT/BCDNet","path":"prototype/model/a_3.py","file_url":"https://github.com/Sense-GVT/BCDNet/blob/HEAD/prototype/model/a_3.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5468426c40c8c5bc","mcp_get_code":{"code_sha256":"5468426c40c8c5bc"}},{"arxiv_id":"2209.00588","paper":"/paper/transformers-are-sample-efficient-world","title":"Transformers are Sample-Efficient World Models","date":"2022-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"eloialonso/iris","path":"src/models/world_model.py","file_url":"https://github.com/eloialonso/iris/blob/HEAD/src/models/world_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"ee23a284027c25be","mcp_get_code":{"code_sha256":"ee23a284027c25be"}},{"arxiv_id":"2207.12577","paper":"/paper/compiler-aware-neural-architecture-search-for","title":"Compiler-Aware Neural Architecture Search for On-Mobile Real-time Super-Resolution","date":"2022-07-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wuyushuwys/compiler-aware-nas-sr","path":"models/wdsr_b.py","file_url":"https://github.com/wuyushuwys/compiler-aware-nas-sr/blob/HEAD/models/wdsr_b.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"448ac85747a3a51c","mcp_get_code":{"code_sha256":"448ac85747a3a51c"}},{"arxiv_id":"2207.10447","paper":"/paper/weakly-supervised-object-localization-via","title":"Weakly Supervised Object Localization via Transformer with Implicit Spatial Calibration","date":"2022-07-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"164140757/SCM","path":"lib/models/deit.py","file_url":"https://github.com/164140757/SCM/blob/HEAD/lib/models/deit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cf9b5254c1c546d6","mcp_get_code":{"code_sha256":"cf9b5254c1c546d6"}},{"arxiv_id":"2207.07116","paper":"/paper/bootstrapped-masked-autoencoders-for-vision","title":"Bootstrapped Masked Autoencoders for Vision BERT Pretraining","date":"2022-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LightDXY/BootMAE","path":"models/modeling_pretrain_bootmae.py","file_url":"https://github.com/LightDXY/BootMAE/blob/HEAD/models/modeling_pretrain_bootmae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2e233dc9eecdb15a","mcp_get_code":{"code_sha256":"2e233dc9eecdb15a"}},{"arxiv_id":"2206.10786","paper":"/paper/generative-pretraining-for-black-box","title":"Generative Pretraining for Black-Box Optimization","date":"2022-06-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"siddarthk97/bonet","path":"mingpt/model_discrete.py","file_url":"https://github.com/siddarthk97/bonet/blob/HEAD/mingpt/model_discrete.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"45a0c4f2576bdfcc","mcp_get_code":{"code_sha256":"45a0c4f2576bdfcc"}},{"arxiv_id":"2206.08569","paper":"/paper/bootstrapped-transformer-for-offline","title":"Bootstrapped Transformer for Offline Reinforcement Learning","date":"2022-06-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jannerm/trajectory-transformer","path":"trajectory/models/transformers.py","file_url":"https://github.com/jannerm/trajectory-transformer/blob/HEAD/trajectory/models/transformers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f25a5a67bed3764e","mcp_get_code":{"code_sha256":"f25a5a67bed3764e"}},{"arxiv_id":"2206.06801","paper":"/paper/peripheral-vision-transformer","title":"Peripheral Vision Transformer","date":"2022-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juhongm999/pervit","path":"model/pervit.py","file_url":"https://github.com/juhongm999/pervit/blob/HEAD/model/pervit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"fcda8f3094f4d7c0","mcp_get_code":{"code_sha256":"fcda8f3094f4d7c0"}},{"arxiv_id":"2205.05277","paper":"/paper/aggpose-deep-aggregation-vision-transformer","title":"AggPose: Deep Aggregation Vision Transformer for Infant Pose Estimation","date":"2022-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SZAR-LAB/AggPose","path":"lib/models/pose_aggpose.py","file_url":"https://github.com/SZAR-LAB/AggPose/blob/HEAD/lib/models/pose_aggpose.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"AGPL-3.0","inline_ok":false,"code_sha256_prefix":"f5274e90d3e2d72b","mcp_get_code":{"code_sha256":"f5274e90d3e2d72b"}},{"arxiv_id":"2205.00302","paper":"/paper/shape-an-unified-approach-to-evaluate-the","title":"SHAPE: An Unified Approach to Evaluate the Contribution and Cooperation of Individual Modalities","date":"2022-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouyilab/shape","path":"core/model/mosei.py","file_url":"https://github.com/zhouyilab/shape/blob/HEAD/core/model/mosei.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb55df157b6b11ba","mcp_get_code":{"code_sha256":"bb55df157b6b11ba"}},{"arxiv_id":"2204.12484","paper":"/paper/vitpose-simple-vision-transformer-baselines","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gpastal24/ViTPose-Pytorch","path":"src/vitpose_infer/builder/backbones/vit.py","file_url":"https://github.com/gpastal24/ViTPose-Pytorch/blob/HEAD/src/vitpose_infer/builder/backbones/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"daeb52387dc365ad","mcp_get_code":{"code_sha256":"daeb52387dc365ad"}},{"arxiv_id":"2204.12484","paper":"/paper/vitpose-simple-vision-transformer-baselines","title":"ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation","date":"2022-04-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JunkyByte/easy_ViTPose","path":"easy_ViTPose/vit_models/model.py","file_url":"https://github.com/JunkyByte/easy_ViTPose/blob/HEAD/easy_ViTPose/vit_models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b45be27e9755bcea","mcp_get_code":{"code_sha256":"b45be27e9755bcea"}},{"arxiv_id":"2204.09331","paper":"/paper/nformer-robust-person-re-identification-with","title":"NFormer: Robust Person Re-identification with Neighbor Transformer","date":"2022-04-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"haochenheheda/nformer","path":"modeling/nformer.py","file_url":"https://github.com/haochenheheda/nformer/blob/HEAD/modeling/nformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a041c298d77f9d64","mcp_get_code":{"code_sha256":"a041c298d77f9d64"}},{"arxiv_id":"2204.07683","paper":"/paper/safe-self-refinement-for-transformer-based","title":"Safe Self-Refinement for Transformer-based Domain Adaptation","date":"2022-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tsun/SSRT","path":"model/SSRT.py","file_url":"https://github.com/tsun/SSRT/blob/HEAD/model/SSRT.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"eb8b342e174c85c9","mcp_get_code":{"code_sha256":"eb8b342e174c85c9"}},{"arxiv_id":"2203.15371","paper":"/paper/mc-beit-multi-choice-discretization-for-image","title":"mc-BEiT: Multi-choice Discretization for Image BERT Pre-training","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lixiaotong97/mc-BEiT","path":"modeling_pretrain.py","file_url":"https://github.com/lixiaotong97/mc-BEiT/blob/HEAD/modeling_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"68aca03e209bac10","mcp_get_code":{"code_sha256":"68aca03e209bac10"}},{"arxiv_id":"2203.15216","paper":"/paper/affine-medical-image-registration-with-coarse","title":"Affine Medical Image Registration with Coarse-to-Fine Vision Transformer","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cwmok/C2FViT","path":"Code/C2FViT_model.py","file_url":"https://github.com/cwmok/C2FViT/blob/HEAD/Code/C2FViT_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ee31bd105e60acec","mcp_get_code":{"code_sha256":"ee31bd105e60acec"}},{"arxiv_id":"2203.13055","paper":"/paper/bailando-3d-dance-generation-by-actor-critic","title":"Bailando: 3D Dance Generation by Actor-Critic GPT with Choreographic Memory","date":"2022-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lisiyao21/bailando","path":"models/cross_cond_gpt2_ac.py","file_url":"https://github.com/lisiyao21/bailando/blob/HEAD/models/cross_cond_gpt2_ac.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7726bbe05a627078","mcp_get_code":{"code_sha256":"7726bbe05a627078"}},{"arxiv_id":"2203.12208","paper":"/paper/self-supervised-learning-of-adversarial","title":"Self-supervised Learning of Adversarial Example: Towards Good Generalizations for Deepfake Detection","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liangchen527/SLADD","path":"src/networks/synthesizer.py","file_url":"https://github.com/liangchen527/SLADD/blob/HEAD/src/networks/synthesizer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6acb761868805246","mcp_get_code":{"code_sha256":"6acb761868805246"}},{"arxiv_id":"2203.12119","paper":"/paper/visual-prompt-tuning","title":"Visual Prompt Tuning","date":"2022-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/apt","path":"modeling_VPT.py","file_url":"https://github.com/wgcban/apt/blob/HEAD/modeling_VPT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"a6526ff13db3ed18","mcp_get_code":{"code_sha256":"a6526ff13db3ed18"}},{"arxiv_id":"2203.10812","paper":"/paper/arm-any-time-super-resolution-method","title":"ARM: Any-Time Super-Resolution Method","date":"2022-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenbong/ARM-Net","path":"models/archs/CARN_arch.py","file_url":"https://github.com/chenbong/ARM-Net/blob/HEAD/models/archs/CARN_arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1415a30ebb3a2c9e","mcp_get_code":{"code_sha256":"1415a30ebb3a2c9e"}},{"arxiv_id":"2203.08243","paper":"/paper/unified-visual-transformer-compression-1","title":"Unified Visual Transformer Compression","date":"2022-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VITA-Group/UVC","path":"UVC/models/modeling.py","file_url":"https://github.com/VITA-Group/UVC/blob/HEAD/UVC/models/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"195dcd4e31d9f812","mcp_get_code":{"code_sha256":"195dcd4e31d9f812"}},{"arxiv_id":"2203.02891","paper":"/paper/multi-class-token-transformer-for-weakly","title":"Multi-class Token Transformer for Weakly Supervised Semantic Segmentation","date":"2022-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xulianuwa/mctformer","path":"models.py","file_url":"https://github.com/xulianuwa/mctformer/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1ae154d72ee154c8","mcp_get_code":{"code_sha256":"1ae154d72ee154c8"}},{"arxiv_id":"2203.00859","paper":"/paper/mixste-seq2seq-mixed-spatio-temporal-encoder","title":"MixSTE: Seq2seq Mixed Spatio-Temporal Encoder for 3D Human Pose Estimation in Video","date":"2022-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JinluZhang1126/MixSTE","path":"common/model_cross.py","file_url":"https://github.com/JinluZhang1126/MixSTE/blob/HEAD/common/model_cross.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"361cb029385178dc","mcp_get_code":{"code_sha256":"361cb029385178dc"}},{"arxiv_id":"2202.09244","paper":"/paper/transfer-and-marginalize-explaining-away-1","title":"Transfer and Marginalize: Explaining Away Label Noise with Privileged Information","date":"2022-02-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danilprov/rethinking_lupi","path":"classification_data/model.py","file_url":"https://github.com/danilprov/rethinking_lupi/blob/HEAD/classification_data/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8b69e801a85a162c","mcp_get_code":{"code_sha256":"8b69e801a85a162c"}},{"arxiv_id":"2202.05492","paper":"/paper/entroformer-a-transformer-based-entropy-model-1","title":"Entroformer: A Transformer-based Entropy Model for Learned Image Compression","date":"2022-02-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mx54039q/entroformer","path":"module/entroformer.py","file_url":"https://github.com/mx54039q/entroformer/blob/HEAD/module/entroformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"1e748260f44313d9","mcp_get_code":{"code_sha256":"1e748260f44313d9"}},{"arxiv_id":"2202.04200","paper":"/paper/maskgit-masked-generative-image-transformer","title":"MaskGIT: Masked Generative Image Transformer","date":"2022-02-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeoai/maskgit-pytorch","path":"Network/transformer.py","file_url":"https://github.com/valeoai/maskgit-pytorch/blob/HEAD/Network/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c1c45512a825fc3b","mcp_get_code":{"code_sha256":"c1c45512a825fc3b"}},{"arxiv_id":"2201.12414","paper":"/paper/any-variational-autoencoder-can-do-arbitrary","title":"Posterior Matching for Arbitrary Conditioning","date":"2022-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lupalab/posterior-matching","path":"posterior_matching/models/vdvae.py","file_url":"https://github.com/lupalab/posterior-matching/blob/HEAD/posterior_matching/models/vdvae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4086bb07fed39601","mcp_get_code":{"code_sha256":"4086bb07fed39601"}},{"arxiv_id":"2201.08821","paper":"/paper/representing-long-range-context-for-graph-1","title":"Representing Long-Range Context for Graph Neural Networks with Global Attention","date":"2022-01-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ucbrise/graphtrans","path":"models/gnn_transformer.py","file_url":"https://github.com/ucbrise/graphtrans/blob/HEAD/models/gnn_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"5e621bc325bada93","mcp_get_code":{"code_sha256":"5e621bc325bada93"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"towhee-io/towhee","path":"towhee/models/convnext/convnext.py","file_url":"https://github.com/towhee-io/towhee/blob/HEAD/towhee/models/convnext/convnext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2dd46bec59a83b7b","mcp_get_code":{"code_sha256":"2dd46bec59a83b7b"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sayakpaul/ConvNeXt-TF","path":"models/convnext.py","file_url":"https://github.com/sayakpaul/ConvNeXt-TF/blob/HEAD/models/convnext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"adc5d3ca9a21de02","mcp_get_code":{"code_sha256":"adc5d3ca9a21de02"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Raghvender1205/ConvNeXt","path":"PyTorch/models/convnext.py","file_url":"https://github.com/Raghvender1205/ConvNeXt/blob/HEAD/PyTorch/models/convnext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"114ebf6f5300f50a","mcp_get_code":{"code_sha256":"114ebf6f5300f50a"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jmnolte/hccnet","path":"models/convnext3d.py","file_url":"https://github.com/jmnolte/hccnet/blob/HEAD/models/convnext3d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"e5136463e3d9b5d9","mcp_get_code":{"code_sha256":"e5136463e3d9b5d9"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"murufeng/awesome_lightweight_networks","path":"light_cnns/Transformer/ConvNeXt.py","file_url":"https://github.com/murufeng/awesome_lightweight_networks/blob/HEAD/light_cnns/Transformer/ConvNeXt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0bf3c80a4d223969","mcp_get_code":{"code_sha256":"0bf3c80a4d223969"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bamps53/convnext-tf","path":"models/convnext.py","file_url":"https://github.com/bamps53/convnext-tf/blob/HEAD/models/convnext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c7a4a3371425c3ed","mcp_get_code":{"code_sha256":"c7a4a3371425c3ed"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sithu31296/semantic-segmentation","path":"semseg/models/backbones/convnext.py","file_url":"https://github.com/sithu31296/semantic-segmentation/blob/HEAD/semseg/models/backbones/convnext.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd9421b72f48ad6d","mcp_get_code":{"code_sha256":"dd9421b72f48ad6d"}},{"arxiv_id":"2201.03545","paper":"/paper/a-convnet-for-the-2020s","title":"A ConvNet for the 2020s","date":"2022-01-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"k-h-ismail/convnext-dcls","path":"models/convnext_dcls.py","file_url":"https://github.com/k-h-ismail/convnext-dcls/blob/HEAD/models/convnext_dcls.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dd371307a76c00b0","mcp_get_code":{"code_sha256":"dd371307a76c00b0"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yangsun22/tc-moa","path":"model/ViT_MAE.py","file_url":"https://github.com/yangsun22/tc-moa/blob/HEAD/model/ViT_MAE.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"861a1945b7d265b5","mcp_get_code":{"code_sha256":"861a1945b7d265b5"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangsr126/mae-lite","path":"projects/mae_lite/models_mae.py","file_url":"https://github.com/wangsr126/mae-lite/blob/HEAD/projects/mae_lite/models_mae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"f85de2d93369d9d8","mcp_get_code":{"code_sha256":"f85de2d93369d9d8"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guilk/vlc","path":"vlc/modules/mae_transformer.py","file_url":"https://github.com/guilk/vlc/blob/HEAD/vlc/modules/mae_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"ad2aca1970e4adb8","mcp_get_code":{"code_sha256":"ad2aca1970e4adb8"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DarshanDeshpande/jax-models","path":"jax_models/models/masked_autoencoder.py","file_url":"https://github.com/DarshanDeshpande/jax-models/blob/HEAD/jax_models/models/masked_autoencoder.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e6f87d41087f7d2d","mcp_get_code":{"code_sha256":"e6f87d41087f7d2d"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FlyEgle/MAE-pytorch","path":"model/Transformers/VIT/mae.py","file_url":"https://github.com/FlyEgle/MAE-pytorch/blob/HEAD/model/Transformers/VIT/mae.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4213367f46ba0d67","mcp_get_code":{"code_sha256":"4213367f46ba0d67"}},{"arxiv_id":"2111.06377","paper":"/paper/masked-autoencoders-are-scalable-vision","title":"Masked Autoencoders Are Scalable Vision Learners","date":"2021-11-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BUPT-PRIV/MAE-priv","path":"mae/modeling_pretrain.py","file_url":"https://github.com/BUPT-PRIV/MAE-priv/blob/HEAD/mae/modeling_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"5199c4f36d1f6651","mcp_get_code":{"code_sha256":"5199c4f36d1f6651"}},{"arxiv_id":"2110.13430","paper":"/paper/contextual-similarity-aggregation-with-self","title":"Contextual Similarity Aggregation with Self-attention for Visual Re-ranking","date":"2021-10-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mcc-wh/csa","path":"network/RerankTransformer.py","file_url":"https://github.com/mcc-wh/csa/blob/HEAD/network/RerankTransformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"267b248e3abc0616","mcp_get_code":{"code_sha256":"267b248e3abc0616"}},{"arxiv_id":"2107.05407","paper":"/paper/pondernet-learning-to-ponder","title":"PonderNet: Learning to Ponder","date":"2021-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/ponder-transformer","path":"ponder_transformer/ponder_transformer.py","file_url":"https://github.com/lucidrains/ponder-transformer/blob/HEAD/ponder_transformer/ponder_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b3198dc3cdb01277","mcp_get_code":{"code_sha256":"b3198dc3cdb01277"}},{"arxiv_id":"2107.00645","paper":"/paper/global-filter-networks-for-image","title":"Global Filter Networks for Image Classification","date":"2021-07-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raoyongming/GFNet","path":"gfnet.py","file_url":"https://github.com/raoyongming/GFNet/blob/HEAD/gfnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"dff7a9ab8c3f50a9","mcp_get_code":{"code_sha256":"dff7a9ab8c3f50a9"}},{"arxiv_id":"2106.08254","paper":"/paper/beit-bert-pre-training-of-image-transformers","title":"BEiT: BERT Pre-Training of Image Transformers","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"microsoft/unilm","path":"beit/modeling_pretrain.py","file_url":"https://github.com/microsoft/unilm/blob/HEAD/beit/modeling_pretrain.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8d8f50f0e337e6ae","mcp_get_code":{"code_sha256":"8d8f50f0e337e6ae"}},{"arxiv_id":"2106.08254","paper":"/paper/beit-bert-pre-training-of-image-transformers","title":"BEiT: BERT Pre-Training of Image Transformers","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/data2vec_vision","path":"beit/modeling_pretrain.py","file_url":"https://github.com/facebookresearch/data2vec_vision/blob/HEAD/beit/modeling_pretrain.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"514990276df7d1e1","mcp_get_code":{"code_sha256":"514990276df7d1e1"}},{"arxiv_id":"2106.08254","paper":"/paper/beit-bert-pre-training-of-image-transformers","title":"BEiT: BERT Pre-Training of Image Transformers","date":"2021-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/vissl","path":"vissl/models/trunks/beit_transformer.py","file_url":"https://github.com/facebookresearch/vissl/blob/HEAD/vissl/models/trunks/beit_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c8d115d51a70401e","mcp_get_code":{"code_sha256":"c8d115d51a70401e"}},{"arxiv_id":"2106.07631","paper":"/paper/improved-transformer-for-high-resolution-gans","title":"Improved Transformer for High-Resolution GANs","date":"2021-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/hit-gan","path":"models/generators.py","file_url":"https://github.com/google-research/hit-gan/blob/HEAD/models/generators.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"6c10eab79338dde3","mcp_get_code":{"code_sha256":"6c10eab79338dde3"}},{"arxiv_id":"2106.02034","paper":"/paper/dynamicvit-efficient-vision-transformers-with","title":"DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification","date":"2021-06-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raoyongming/DynamicViT","path":"models/dyvit.py","file_url":"https://github.com/raoyongming/DynamicViT/blob/HEAD/models/dyvit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a559e910dfa91050","mcp_get_code":{"code_sha256":"a559e910dfa91050"}},{"arxiv_id":"2106.01345","paper":"/paper/decision-transformer-reinforcement-learning","title":"Decision Transformer: Reinforcement Learning via Sequence Modeling","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LAS1520/Gato-A-Generalist-Agent","path":"Gato_models/model.py","file_url":"https://github.com/LAS1520/Gato-A-Generalist-Agent/blob/HEAD/Gato_models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e3c3fcca273a8e47","mcp_get_code":{"code_sha256":"e3c3fcca273a8e47"}},{"arxiv_id":"2106.01345","paper":"/paper/decision-transformer-reinforcement-learning","title":"Decision Transformer: Reinforcement Learning via Sequence Modeling","date":"2021-06-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"opendilab/DI-engine","path":"ding/model/template/decision_transformer.py","file_url":"https://github.com/opendilab/DI-engine/blob/HEAD/ding/model/template/decision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"25243005f892c666","mcp_get_code":{"code_sha256":"25243005f892c666"}},{"arxiv_id":"2105.15203","paper":"/paper/segformer-simple-and-efficient-design-for","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","date":"2021-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IMvision12/SegFormer-tf","path":"models/segformer.py","file_url":"https://github.com/IMvision12/SegFormer-tf/blob/HEAD/models/segformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0730c486d30ba0ad","mcp_get_code":{"code_sha256":"0730c486d30ba0ad"}},{"arxiv_id":"2105.15203","paper":"/paper/segformer-simple-and-efficient-design-for","title":"SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers","date":"2021-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DarshanDeshpande/jax-models","path":"jax_models/models/segformer.py","file_url":"https://github.com/DarshanDeshpande/jax-models/blob/HEAD/jax_models/models/segformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"d3ae12f830ae7473","mcp_get_code":{"code_sha256":"d3ae12f830ae7473"}},{"arxiv_id":"2105.14432","paper":"/paper/transformer-based-deep-image-matching-for","title":"TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification","date":"2021-05-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JDAI-CV/fast-reid","path":"fastreid/modeling/backbones/vision_transformer.py","file_url":"https://github.com/JDAI-CV/fast-reid/blob/HEAD/fastreid/modeling/backbones/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ce093271c9f68c89","mcp_get_code":{"code_sha256":"ce093271c9f68c89"}},{"arxiv_id":"2105.03889","paper":"/paper/conformer-local-features-coupling-global","title":"Conformer: Local Features Coupling Global Representations for Visual Recognition","date":"2021-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vasgaowei/TS-CAM","path":"lib/models/conformer.py","file_url":"https://github.com/vasgaowei/TS-CAM/blob/HEAD/lib/models/conformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4f37b2a82fa6afcf","mcp_get_code":{"code_sha256":"4f37b2a82fa6afcf"}},{"arxiv_id":"2105.03889","paper":"/paper/conformer-local-features-coupling-global","title":"Conformer: Local Features Coupling Global Representations for Visual Recognition","date":"2021-05-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengzhiliang/Conformer","path":"conformer.py","file_url":"https://github.com/pengzhiliang/Conformer/blob/HEAD/conformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"40b891c254ce0189","mcp_get_code":{"code_sha256":"40b891c254ce0189"}},{"arxiv_id":"2104.12099","paper":"/paper/visual-saliency-transformer","title":"Visual Saliency Transformer","date":"2021-04-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fhshen2022/prunerepaint","path":"RGB_VST/Models/t2t_vit.py","file_url":"https://github.com/fhshen2022/prunerepaint/blob/HEAD/RGB_VST/Models/t2t_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7c18bbee6c5d2255","mcp_get_code":{"code_sha256":"7c18bbee6c5d2255"}},{"arxiv_id":"2104.09224","paper":"/paper/multi-modal-fusion-transformer-for-end-to-end","title":"Multi-Modal Fusion Transformer for End-to-End Autonomous Driving","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kin-Zhang/mmfn","path":"team_code/benchmarks/transfuser/model.py","file_url":"https://github.com/Kin-Zhang/mmfn/blob/HEAD/team_code/benchmarks/transfuser/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5075118d0c207c37","mcp_get_code":{"code_sha256":"5075118d0c207c37"}},{"arxiv_id":"2104.09224","paper":"/paper/multi-modal-fusion-transformer-for-end-to-end","title":"Multi-Modal Fusion Transformer for End-to-End Autonomous Driving","date":"2021-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"autonomousvision/transfuser","path":"team_code_transfuser/transfuser.py","file_url":"https://github.com/autonomousvision/transfuser/blob/HEAD/team_code_transfuser/transfuser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d93ea624e09cfe3d","mcp_get_code":{"code_sha256":"d93ea624e09cfe3d"}},{"arxiv_id":"2103.15691","paper":"/paper/2103-15691","title":"ViViT: A Video Vision Transformer","date":"2021-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KSonPham/ViVit-a-Pytorch-implementation","path":"models/modeling.py","file_url":"https://github.com/KSonPham/ViVit-a-Pytorch-implementation/blob/HEAD/models/modeling.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3aeaa3af2d538769","mcp_get_code":{"code_sha256":"3aeaa3af2d538769"}},{"arxiv_id":"2103.14031","paper":"/paper/high-fidelity-pluralistic-image-completion","title":"High-Fidelity Pluralistic Image Completion with Transformers","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karynaur/High-Fidelity-Pluralistic-ICT","path":"transformer/model.py","file_url":"https://github.com/karynaur/High-Fidelity-Pluralistic-ICT/blob/HEAD/transformer/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"69cf3439b6b654f7","mcp_get_code":{"code_sha256":"69cf3439b6b654f7"}},{"arxiv_id":"2103.14031","paper":"/paper/high-fidelity-pluralistic-image-completion","title":"High-Fidelity Pluralistic Image Completion with Transformers","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"raywzy/ICT","path":"Transformer/models/model.py","file_url":"https://github.com/raywzy/ICT/blob/HEAD/Transformer/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"545f9243b2c67d16","mcp_get_code":{"code_sha256":"545f9243b2c67d16"}},{"arxiv_id":"2103.14030","paper":"/paper/swin-transformer-hierarchical-vision","title":"Swin Transformer: Hierarchical Vision Transformer using Shifted Windows","date":"2021-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WangFeng18/Swin-Transformer","path":"SwinTransformer.py","file_url":"https://github.com/WangFeng18/Swin-Transformer/blob/HEAD/SwinTransformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"62d274e03594f950","mcp_get_code":{"code_sha256":"62d274e03594f950"}},{"arxiv_id":"2103.13076","paper":"/paper/finetuning-pretrained-transformers-into-rnns","title":"Finetuning Pretrained Transformers into RNNs","date":"2021-03-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yashbonde/RNN-sim","path":"t2rmodel.py","file_url":"https://github.com/yashbonde/RNN-sim/blob/HEAD/t2rmodel.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"129bc8a38ada9fce","mcp_get_code":{"code_sha256":"129bc8a38ada9fce"}},{"arxiv_id":"2103.12091","paper":"/paper/transformers-solve-the-limited-receptive","title":"Transformer-Based Attention Networks for Continuous Pixel-Wise Prediction","date":"2021-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ygjwd12345/TransDepth","path":"pytorch/TransUNet/networks/vit_seg_modeling.py","file_url":"https://github.com/ygjwd12345/TransDepth/blob/HEAD/pytorch/TransUNet/networks/vit_seg_modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38c7e4b5cd543511","mcp_get_code":{"code_sha256":"38c7e4b5cd543511"}},{"arxiv_id":"2103.11816","paper":"/paper/incorporating-convolution-designs-into-visual","title":"Incorporating Convolution Designs into Visual Transformers","date":"2021-03-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"coeusguo/ceit","path":"ceit_model.py","file_url":"https://github.com/coeusguo/ceit/blob/HEAD/ceit_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e5a66231dac09f8c","mcp_get_code":{"code_sha256":"e5a66231dac09f8c"}},{"arxiv_id":"2103.10619","paper":"/paper/scalable-visual-transformers-with","title":"Scalable Vision Transformers with Hierarchical Pooling","date":"2021-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MonashAI/HVT","path":"models.py","file_url":"https://github.com/MonashAI/HVT/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"4f3b1c60f898dabe","mcp_get_code":{"code_sha256":"4f3b1c60f898dabe"}},{"arxiv_id":"2103.10455","paper":"/paper/3d-human-pose-estimation-with-spatial-and","title":"3D Human Pose Estimation with Spatial and Temporal Transformers","date":"2021-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zczcwh/PoseFormer","path":"common/model_poseformer.py","file_url":"https://github.com/zczcwh/PoseFormer/blob/HEAD/common/model_poseformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"806376d9202b2bc9","mcp_get_code":{"code_sha256":"806376d9202b2bc9"}},{"arxiv_id":"2103.10455","paper":"/paper/3d-human-pose-estimation-with-spatial-and","title":"3D Human Pose Estimation with Spatial and Temporal Transformers","date":"2021-03-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thuxyz19/test","path":"common/model_poseformer.py","file_url":"https://github.com/thuxyz19/test/blob/HEAD/common/model_poseformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4b147c20666e9018","mcp_get_code":{"code_sha256":"4b147c20666e9018"}},{"arxiv_id":"2103.04039","paper":"/paper/classsr-a-general-framework-to-accelerate","title":"ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristic","date":"2021-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Xiangtaokong/ClassSR","path":"codes/models/archs/classSR_carn_arch.py","file_url":"https://github.com/Xiangtaokong/ClassSR/blob/HEAD/codes/models/archs/classSR_carn_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"da7195515f58c81b","mcp_get_code":{"code_sha256":"da7195515f58c81b"}},{"arxiv_id":"2103.02406","paper":"/paper/multi-attentional-deepfake-detection","title":"Multi-attentional Deepfake Detection","date":"2021-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yoctta/multiple-attention","path":"models/MAT.py","file_url":"https://github.com/yoctta/multiple-attention/blob/HEAD/models/MAT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9bf22d4f451ed68d","mcp_get_code":{"code_sha256":"9bf22d4f451ed68d"}},{"arxiv_id":"2103.00112","paper":"/paper/transformer-in-transformer","title":"Transformer in Transformer","date":"2021-02-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huawei-noah/CV-Backbones","path":"tnt_pytorch/tnt.py","file_url":"https://github.com/huawei-noah/CV-Backbones/blob/HEAD/tnt_pytorch/tnt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0adf903cee3f68c1","mcp_get_code":{"code_sha256":"0adf903cee3f68c1"}},{"arxiv_id":"2102.12122","paper":"/paper/pyramid-vision-transformer-a-versatile","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","date":"2021-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"whai362/PVT","path":"classification/pvt.py","file_url":"https://github.com/whai362/PVT/blob/HEAD/classification/pvt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"583eadd9a5e6a087","mcp_get_code":{"code_sha256":"583eadd9a5e6a087"}},{"arxiv_id":"2102.12122","paper":"/paper/pyramid-vision-transformer-a-versatile","title":"Pyramid Vision Transformer: A Versatile Backbone for Dense Prediction without Convolutions","date":"2021-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DarshanDeshpande/jax-models","path":"jax_models/models/pvit.py","file_url":"https://github.com/DarshanDeshpande/jax-models/blob/HEAD/jax_models/models/pvit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"3ec0d795856d98f3","mcp_get_code":{"code_sha256":"3ec0d795856d98f3"}},{"arxiv_id":"2101.11986","paper":"/paper/tokens-to-token-vit-training-vision","title":"Tokens-to-Token ViT: Training Vision Transformers from Scratch on ImageNet","date":"2021-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yitu-opensource/T2T-ViT","path":"models/t2t_vit.py","file_url":"https://github.com/yitu-opensource/T2T-ViT/blob/HEAD/models/t2t_vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"4e7b29e9c82cf102","mcp_get_code":{"code_sha256":"4e7b29e9c82cf102"}},{"arxiv_id":"2101.11605","paper":"/paper/bottleneck-transformers-for-visual","title":"Bottleneck Transformers for Visual Recognition","date":"2021-01-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"scheshmi/BottleneckTransformers","path":"model.py","file_url":"https://github.com/scheshmi/BottleneckTransformers/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5f213d496b96f84d","mcp_get_code":{"code_sha256":"5f213d496b96f84d"}},{"arxiv_id":"2101.10281","paper":"/paper/pawls-pdf-annotation-with-labels-and","title":"PAWLS: PDF Annotation With Labels and Structure","date":"2021-01-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/pawls","path":"cli/pawls/preprocessors/model.py","file_url":"https://github.com/allenai/pawls/blob/HEAD/cli/pawls/preprocessors/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"cf44d123a4b4bcc2","mcp_get_code":{"code_sha256":"cf44d123a4b4bcc2"}},{"arxiv_id":"2012.12877","paper":"/paper/training-data-efficient-image-transformers","title":"Training data-efficient image transformers & distillation through attention","date":"2020-12-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/EasyCV","path":"easycv/models/backbones/vision_transformer.py","file_url":"https://github.com/alibaba/EasyCV/blob/HEAD/easycv/models/backbones/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"bc7c311c2770067a","mcp_get_code":{"code_sha256":"bc7c311c2770067a"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mahmoodlab/hipt","path":"HIPT_4K/vision_transformer.py","file_url":"https://github.com/mahmoodlab/hipt/blob/HEAD/HIPT_4K/vision_transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"65e124fb59929e7b","mcp_get_code":{"code_sha256":"65e124fb59929e7b"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nateraw/lightning-vision-transformer","path":"vit.py","file_url":"https://github.com/nateraw/lightning-vision-transformer/blob/HEAD/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"82e3e86f7ef279d6","mcp_get_code":{"code_sha256":"82e3e86f7ef279d6"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"naver-ai/pflayer","path":"vit_pf.py","file_url":"https://github.com/naver-ai/pflayer/blob/HEAD/vit_pf.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"111794c24e7fff19","mcp_get_code":{"code_sha256":"111794c24e7fff19"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nachiket273/Vision_transformer_pytorch","path":"vit.py","file_url":"https://github.com/nachiket273/Vision_transformer_pytorch/blob/HEAD/vit.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"908f6f29e84e7834","mcp_get_code":{"code_sha256":"908f6f29e84e7834"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arkel23/PyTorch-Pretrained-ViT","path":"pytorch_pretrained_vit/model.py","file_url":"https://github.com/arkel23/PyTorch-Pretrained-ViT/blob/HEAD/pytorch_pretrained_vit/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6d3c10e6aeb535b","mcp_get_code":{"code_sha256":"d6d3c10e6aeb535b"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"junyongyou/triq","path":"src/vit_iqa/ViT_pytorch/models/modeling.py","file_url":"https://github.com/junyongyou/triq/blob/HEAD/src/vit_iqa/ViT_pytorch/models/modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4af23178d3041345","mcp_get_code":{"code_sha256":"4af23178d3041345"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HzcIrving/DeepLearning_PlayGround","path":"VIT/Model.py","file_url":"https://github.com/HzcIrving/DeepLearning_PlayGround/blob/HEAD/VIT/Model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0c8c99ad8b44657f","mcp_get_code":{"code_sha256":"0c8c99ad8b44657f"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangguanan/light-reid","path":"lightreid/models/backbones/transformers/vit_timm.py","file_url":"https://github.com/wangguanan/light-reid/blob/HEAD/lightreid/models/backbones/transformers/vit_timm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f9eebdcbcec2008d","mcp_get_code":{"code_sha256":"f9eebdcbcec2008d"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukemelas/PyTorch-Pretrained-ViT","path":"pytorch_pretrained_vit/model.py","file_url":"https://github.com/lukemelas/PyTorch-Pretrained-ViT/blob/HEAD/pytorch_pretrained_vit/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"dd163b043f6772c8","mcp_get_code":{"code_sha256":"dd163b043f6772c8"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jankrepl/mildlyoverfitted","path":"github_adventures/vision_transformer/custom.py","file_url":"https://github.com/jankrepl/mildlyoverfitted/blob/HEAD/github_adventures/vision_transformer/custom.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c559706fd8342db1","mcp_get_code":{"code_sha256":"c559706fd8342db1"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"skchen1993/TrangFG","path":"models/modeling.py","file_url":"https://github.com/skchen1993/TrangFG/blob/HEAD/models/modeling.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8711444686518b7c","mcp_get_code":{"code_sha256":"8711444686518b7c"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"OML-Team/open-metric-learning","path":"oml/models/vit_dino/external_v2/vision_transformer.py","file_url":"https://github.com/OML-Team/open-metric-learning/blob/HEAD/oml/models/vit_dino/external_v2/vision_transformer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"21c112b898681805","mcp_get_code":{"code_sha256":"21c112b898681805"}},{"arxiv_id":"2010.11929","paper":"/paper/an-image-is-worth-16x16-words-transformers-1","title":"An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale","date":"2020-10-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tintn/vision-transformer-from-scratch","path":"vit.py","file_url":"https://github.com/tintn/vision-transformer-from-scratch/blob/HEAD/vit.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3222eaf8c8b350da","mcp_get_code":{"code_sha256":"3222eaf8c8b350da"}},{"arxiv_id":"2009.09761","paper":"/paper/diffwave-a-versatile-diffusion-model-for","title":"DiffWave: A Versatile Diffusion Model for Audio Synthesis","date":"2020-09-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"revsic/tf-diffwave","path":"model/wavenet.py","file_url":"https://github.com/revsic/tf-diffwave/blob/HEAD/model/wavenet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c2e9d29ad73afee3","mcp_get_code":{"code_sha256":"c2e9d29ad73afee3"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lucidrains/make-a-video-pytorch","path":"make_a_video_pytorch/make_a_video.py","file_url":"https://github.com/lucidrains/make-a-video-pytorch/blob/HEAD/make_a_video_pytorch/make_a_video.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"682c7d765393b80c","mcp_get_code":{"code_sha256":"682c7d765393b80c"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"vvvm23/ddpm","path":"ddpm/unet.py","file_url":"https://github.com/vvvm23/ddpm/blob/HEAD/ddpm/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d66519493e45fa72","mcp_get_code":{"code_sha256":"d66519493e45fa72"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjfghk5697/Pytorch-Research-Paper-Implementations","path":"Diffusion/DDPM/models/model.py","file_url":"https://github.com/cjfghk5697/Pytorch-Research-Paper-Implementations/blob/HEAD/Diffusion/DDPM/models/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"68c21a4e900cf84d","mcp_get_code":{"code_sha256":"68c21a4e900cf84d"}},{"arxiv_id":"2006.11239","paper":"/paper/denoising-diffusion-probabilistic-models","title":"Denoising Diffusion Probabilistic Models","date":"2020-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"KiUngSong/Generative-Models","path":"DDPM/DDPM_pytorch.py","file_url":"https://github.com/KiUngSong/Generative-Models/blob/HEAD/DDPM/DDPM_pytorch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"45c3ad11676d68ef","mcp_get_code":{"code_sha256":"45c3ad11676d68ef"}},{"arxiv_id":"1908.09124","paper":"/paper/seesawfacenets-sparse-and-robust-face","title":"SeesawFaceNets: sparse and robust face verification model for mobile platform","date":"2019-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pshashk/seesaw-facenet","path":"model.py","file_url":"https://github.com/pshashk/seesaw-facenet/blob/HEAD/model.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"41b5161a608d5f29","mcp_get_code":{"code_sha256":"41b5161a608d5f29"}},{"arxiv_id":"1901.02860","paper":"/paper/transformer-xl-attentive-language-models","title":"Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context","date":"2019-01-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"facebookresearch/code-prediction-transformer","path":"model.py","file_url":"https://github.com/facebookresearch/code-prediction-transformer/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"548da299b3bc04e9","mcp_get_code":{"code_sha256":"548da299b3bc04e9"}},{"arxiv_id":"1812.04948","paper":"/paper/a-style-based-generator-architecture-for","title":"A Style-Based Generator Architecture for Generative Adversarial Networks","date":"2018-12-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Ipsedo/MusicGAN","path":"music_gan/networks/generator.py","file_url":"https://github.com/Ipsedo/MusicGAN/blob/HEAD/music_gan/networks/generator.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"84766fd3752f8c8e","mcp_get_code":{"code_sha256":"84766fd3752f8c8e"}},{"arxiv_id":"1810.04805","paper":"/paper/bert-pre-training-of-deep-bidirectional","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","date":"2018-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dhlee347/pytorchic-bert","path":"models.py","file_url":"https://github.com/dhlee347/pytorchic-bert/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"2dcf1b4f0aa2fa9a","mcp_get_code":{"code_sha256":"2dcf1b4f0aa2fa9a"}},{"arxiv_id":"1807.03039","paper":"/paper/glow-generative-flow-with-invertible-1x1","title":"Glow: Generative Flow with Invertible 1x1 Convolutions","date":"2018-07-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rosinality/glow-pytorch","path":"model.py","file_url":"https://github.com/rosinality/glow-pytorch/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f4e6c019fb063b86","mcp_get_code":{"code_sha256":"f4e6c019fb063b86"}},{"arxiv_id":"1806.09055","paper":"/paper/darts-differentiable-architecture-search","title":"DARTS: Differentiable Architecture Search","date":"2018-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yochaiz/darts-UNIQ","path":"cnn/MixedLayer.py","file_url":"https://github.com/yochaiz/darts-UNIQ/blob/HEAD/cnn/MixedLayer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"640db5645ab09cbc","mcp_get_code":{"code_sha256":"640db5645ab09cbc"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"crcrpar/pytorch.sngan_projection","path":"models/discriminators/resblocks.py","file_url":"https://github.com/crcrpar/pytorch.sngan_projection/blob/HEAD/models/discriminators/resblocks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"140b5bac7a8508a9","mcp_get_code":{"code_sha256":"140b5bac7a8508a9"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"glouppe/info8010-deep-learning","path":"code/gpt/gpt-v7.py","file_url":"https://github.com/glouppe/info8010-deep-learning/blob/HEAD/code/gpt/gpt-v7.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"BSD-3-Clause","inline_ok":true,"code_sha256_prefix":"bb4add74d2e7f44e","mcp_get_code":{"code_sha256":"bb4add74d2e7f44e"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hannibal046/nanorwkv","path":"modeling_gpt.py","file_url":"https://github.com/hannibal046/nanorwkv/blob/HEAD/modeling_gpt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3a9836de180bbd38","mcp_get_code":{"code_sha256":"3a9836de180bbd38"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karpathy/makemore","path":"makemore.py","file_url":"https://github.com/karpathy/makemore/blob/HEAD/makemore.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4ee6b14007ffaea4","mcp_get_code":{"code_sha256":"4ee6b14007ffaea4"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akanyaani/minGPTF","path":"mingptf/model.py","file_url":"https://github.com/akanyaani/minGPTF/blob/HEAD/mingptf/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f997fb30a69791f0","mcp_get_code":{"code_sha256":"f997fb30a69791f0"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lukemelas/PyTorch-Pretrained-ViT","path":"pytorch_pretrained_vit/transformer.py","file_url":"https://github.com/lukemelas/PyTorch-Pretrained-ViT/blob/HEAD/pytorch_pretrained_vit/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f6dc7c84d0f974a6","mcp_get_code":{"code_sha256":"f6dc7c84d0f974a6"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arkel23/PyTorch-Pretrained-ViT","path":"pytorch_pretrained_vit/transformer.py","file_url":"https://github.com/arkel23/PyTorch-Pretrained-ViT/blob/HEAD/pytorch_pretrained_vit/transformer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"4e1c6a3969ca8782","mcp_get_code":{"code_sha256":"4e1c6a3969ca8782"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"souvikshanku/translit-former","path":"model.py","file_url":"https://github.com/souvikshanku/translit-former/blob/HEAD/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8eddd882df3e8dd7","mcp_get_code":{"code_sha256":"8eddd882df3e8dd7"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"graykode/gpt-2-Pytorch","path":"GPT2/model.py","file_url":"https://github.com/graykode/gpt-2-Pytorch/blob/HEAD/GPT2/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"35c7110bce297f56","mcp_get_code":{"code_sha256":"35c7110bce297f56"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"semicontinuity/nlp","path":"lopuhin_transformer_lm/lm/model.py","file_url":"https://github.com/semicontinuity/nlp/blob/HEAD/lopuhin_transformer_lm/lm/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a220ea329add48ca","mcp_get_code":{"code_sha256":"a220ea329add48ca"}},{"arxiv_id":"1706.03762","paper":"/paper/attention-is-all-you-need","title":"Attention Is All You Need","date":"2017-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PrideLee/sentiment-analysis","path":"transformer/model.py","file_url":"https://github.com/PrideLee/sentiment-analysis/blob/HEAD/transformer/model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c09761a00841149","mcp_get_code":{"code_sha256":"8c09761a00841149"}},{"arxiv_id":"1704.04861","paper":"/paper/mobilenets-efficient-convolutional-neural","title":"MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications","date":"2017-04-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"allenai/dnw","path":"models/graphs/mobilenetv1like.py","file_url":"https://github.com/allenai/dnw/blob/HEAD/models/graphs/mobilenetv1like.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"67e69022e2fdbfbc","mcp_get_code":{"code_sha256":"67e69022e2fdbfbc"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JianGoForIt/YellowFin_Pytorch","path":"pytorch-cifar/models/resnext.py","file_url":"https://github.com/JianGoForIt/YellowFin_Pytorch/blob/HEAD/pytorch-cifar/models/resnext.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"20a10df4cee25132","mcp_get_code":{"code_sha256":"20a10df4cee25132"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cjf8899/Pytorch_ResNeXt","path":"models/resneXt.py","file_url":"https://github.com/cjf8899/Pytorch_ResNeXt/blob/HEAD/models/resneXt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e8b334a9a47fc3c4","mcp_get_code":{"code_sha256":"e8b334a9a47fc3c4"}},{"arxiv_id":"1611.05431","paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Mayurji/Image-Classification-PyTorch","path":"ResNeXt.py","file_url":"https://github.com/Mayurji/Image-Classification-PyTorch/blob/HEAD/ResNeXt.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"2f70fa5b1a9337a8","mcp_get_code":{"code_sha256":"2f70fa5b1a9337a8"}},{"arxiv_id":"1512.03385","paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Bao-Jiarong/ResNet","path":"resnet.py","file_url":"https://github.com/Bao-Jiarong/ResNet/blob/HEAD/resnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"400e853d45c39183","mcp_get_code":{"code_sha256":"400e853d45c39183"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pwochner/cmr_segmentation","path":"unet.py","file_url":"https://github.com/pwochner/cmr_segmentation/blob/HEAD/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0baaabd50d989fcd","mcp_get_code":{"code_sha256":"0baaabd50d989fcd"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Miltos-90/UNet_Biomedical_Image_Segmentation","path":"UNet.py","file_url":"https://github.com/Miltos-90/UNet_Biomedical_Image_Segmentation/blob/HEAD/UNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5121b74b1e916de5","mcp_get_code":{"code_sha256":"5121b74b1e916de5"}},{"arxiv_id":"1505.04597","paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"trupewate/lung_segmentation_tutorial","path":"src/models.py","file_url":"https://github.com/trupewate/lung_segmentation_tutorial/blob/HEAD/src/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"786b95ec244eca53","mcp_get_code":{"code_sha256":"786b95ec244eca53"}},{"arxiv_id":"ijcai2025_0890","paper":null,"title":"arXiv:ijcai2025_0890","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Auguuust/DiffEC","path":"Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","file_url":"https://github.com/Auguuust/DiffEC/blob/HEAD/Diffusion_based/DiffusionModels/noisePredictModels/Unet/_1DUNet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bb5cb3666b2a4b3c","mcp_get_code":{"code_sha256":"bb5cb3666b2a4b3c"}}]}