{"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/get-loader","entry":"get_loader","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":62,"n_papers_ran":9,"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":69,"n_samples_ran":10,"n_samples_fingerprinted":0,"n_places":71,"n_places_pointer_only":19,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":9,"unverified":59},"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":"2607.22722","paper":"/paper/arxiv-2607-22722","title":"A New Kind of Adversarial Example: Measuring the Human-Model Gap, and Its Relationship to OOD Detection","date":null,"month_inferred_from_arxiv_id":"2026-07","title_source":"syntology","repo":"alikayyam/NKE_attack","path":"nke/data.py","file_url":"https://github.com/alikayyam/NKE_attack/blob/HEAD/nke/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"124f18256cea5d08","mcp_get_code":{"code_sha256":"124f18256cea5d08"}},{"arxiv_id":"2606.18209","paper":"/paper/arxiv-2606-18209","title":"Rethinking Dataset Distillation for Classification: Do Distilled Sets Outperform Coresets?","date":null,"month_inferred_from_arxiv_id":"2026-06","title_source":"syntology","repo":"AyushRoy2001/ManifoldGD","path":"fid.py","file_url":"https://github.com/AyushRoy2001/ManifoldGD/blob/HEAD/fid.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8e9046371d41825d","mcp_get_code":{"code_sha256":"8e9046371d41825d"}},{"arxiv_id":"2601.21315","paper":"/paper/arxiv-2601-21315","title":"Distributionally Robust Classification for Multi-source Unsupervised Domain Adaptation","date":"2026-01-29","month_inferred_from_arxiv_id":null,"title_source":"syntology","repo":"kshwi5500/DRL_for_UDA","path":"get_data_ours.py","file_url":"https://github.com/kshwi5500/DRL_for_UDA/blob/HEAD/get_data_ours.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"68831a455604e3e7","mcp_get_code":{"code_sha256":"68831a455604e3e7"}},{"arxiv_id":"2511.17914","paper":"/paper/arxiv-2511-17914","title":"Rectifying Soft-Label Entangled Bias in Long-Tailed Dataset Distillation","date":null,"month_inferred_from_arxiv_id":"2025-11","title_source":"syntology","repo":"j-cyoung/ADSA_DD","path":"perturbation_analysis/compute_entropy.py","file_url":"https://github.com/j-cyoung/ADSA_DD/blob/HEAD/perturbation_analysis/compute_entropy.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"92b50eb6e14294c7","mcp_get_code":{"code_sha256":"92b50eb6e14294c7"}},{"arxiv_id":"2506.21866","paper":null,"title":"arXiv:2506.21866","date":null,"month_inferred_from_arxiv_id":"2025-06","title_source":null,"repo":"CSYSI/DPU-Former","path":"DPU-Former_25_IJCAI/utils/dataloader.py","file_url":"https://github.com/CSYSI/DPU-Former/blob/HEAD/DPU-Former_25_IJCAI/utils/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e04135543eeb06ab","mcp_get_code":{"code_sha256":"e04135543eeb06ab"}},{"arxiv_id":"2502.01189","paper":"/paper/compressed-image-generation-with-denoising","title":"Compressed Image Generation with Denoising Diffusion Codebook Models","date":"2025-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DDCM-2025/ddcm-compressed-image-generation","path":"ddcm/latent_runners.py","file_url":"https://github.com/DDCM-2025/ddcm-compressed-image-generation/blob/HEAD/ddcm/latent_runners.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5550ef781c1cfd75","mcp_get_code":{"code_sha256":"5550ef781c1cfd75"}},{"arxiv_id":"2406.17343","paper":"/paper/q-dit-accurate-post-training-quantization-for","title":"Q-DiT: Accurate Post-Training Quantization for Diffusion Transformers","date":"2024-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"juanerx/q-dit","path":"qdit/datautils.py","file_url":"https://github.com/juanerx/q-dit/blob/HEAD/qdit/datautils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"b4ced3357c4fdc20","mcp_get_code":{"code_sha256":"b4ced3357c4fdc20"}},{"arxiv_id":"2406.09754","paper":"/paper/lavib-a-large-scale-video-interpolation","title":"LAVIB: A Large-scale Video Interpolation Benchmark","date":"2024-06-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandrosstergiou/lavib","path":"VFI_codebases/FLAVR/dataset/lavib.py","file_url":"https://github.com/alexandrosstergiou/lavib/blob/HEAD/VFI_codebases/FLAVR/dataset/lavib.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f4e7766f2d6d996e","mcp_get_code":{"code_sha256":"f4e7766f2d6d996e"}},{"arxiv_id":"2405.06880","paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sldgroup/emcad","path":"utils/dataloader.py","file_url":"https://github.com/sldgroup/emcad/blob/HEAD/utils/dataloader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"fc82fd0863bc4d22","mcp_get_code":{"code_sha256":"fc82fd0863bc4d22"}},{"arxiv_id":"2405.06880","paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sldgroup/emcad","path":"utils/dataloader_polyp.py","file_url":"https://github.com/sldgroup/emcad/blob/HEAD/utils/dataloader_polyp.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"f9ba40e4610a2020","mcp_get_code":{"code_sha256":"f9ba40e4610a2020"}},{"arxiv_id":"2404.12766","paper":"/paper/continual-learning-on-a-diet-learning-from","title":"Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation","date":"2024-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wx-zhang/continual-learning-on-a-diet","path":"data.py","file_url":"https://github.com/wx-zhang/continual-learning-on-a-diet/blob/HEAD/data.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d6d8bcbbbf9b99ab","mcp_get_code":{"code_sha256":"d6d8bcbbbf9b99ab"}},{"arxiv_id":"2403.06462","paper":"/paper/towards-the-uncharted-density-descending","title":"Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gavinwxy/ddfp","path":"dataset/builder.py","file_url":"https://github.com/gavinwxy/ddfp/blob/HEAD/dataset/builder.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":"c4a5f452f24a2ef0","mcp_get_code":{"code_sha256":"c4a5f452f24a2ef0"}},{"arxiv_id":"2403.06462","paper":"/paper/towards-the-uncharted-density-descending","title":"Towards the Uncharted: Density-Descending Feature Perturbation for Semi-supervised Semantic Segmentation","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gavinwxy/ddfp","path":"dataset_city/builder.py","file_url":"https://github.com/gavinwxy/ddfp/blob/HEAD/dataset_city/builder.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":"5469d8a1c4ce07cc","mcp_get_code":{"code_sha256":"5469d8a1c4ce07cc"}},{"arxiv_id":"2403.03536","paper":"/paper/towards-efficient-and-effective-unlearning-of","title":"Towards Efficient and Effective Unlearning of Large Language Models for Recommendation","date":"2024-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"justarter/e2urec","path":"utils.py","file_url":"https://github.com/justarter/e2urec/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"e24cdbaf08b34918","mcp_get_code":{"code_sha256":"e24cdbaf08b34918"}},{"arxiv_id":"2402.15853","paper":"/paper/rauca-a-novel-physical-adversarial-attack-on","title":"RAUCA: A Novel Physical Adversarial Attack on Vehicle Detectors via Robust and Accurate Camouflage Generation","date":"2024-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"SeRAlab/Robust-and-Accurate-UV-map-based-Camouflage-Attack","path":"src/Image_Segmentation/data_loader.py","file_url":"https://github.com/SeRAlab/Robust-and-Accurate-UV-map-based-Camouflage-Attack/blob/HEAD/src/Image_Segmentation/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"12ab0a19dd968bce","mcp_get_code":{"code_sha256":"12ab0a19dd968bce"}},{"arxiv_id":"2402.14371","paper":"/paper/hr-apr-apr-agnostic-framework-with","title":"HR-APR: APR-agnostic Framework with Uncertainty Estimation and Hierarchical Refinement for Camera Relocalisation","date":"2024-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lck666666/hr-apr","path":"feature_extractor/data_loader.py","file_url":"https://github.com/lck666666/hr-apr/blob/HEAD/feature_extractor/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2f5421de91edb081","mcp_get_code":{"code_sha256":"2f5421de91edb081"}},{"arxiv_id":"2401.03082","paper":"/paper/umie-unified-multimodal-information","title":"UMIE: Unified Multimodal Information Extraction with Instruction Tuning","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZUCC-AI/UMIE","path":"src/ace05_clip_data.py","file_url":"https://github.com/ZUCC-AI/UMIE/blob/HEAD/src/ace05_clip_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d93f7729eb095f9a","mcp_get_code":{"code_sha256":"d93f7729eb095f9a"}},{"arxiv_id":"2401.03082","paper":"/paper/umie-unified-multimodal-information","title":"UMIE: Unified Multimodal Information Extraction with Instruction Tuning","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZUCC-AI/UMIE","path":"src/imsitu_clip_data.py","file_url":"https://github.com/ZUCC-AI/UMIE/blob/HEAD/src/imsitu_clip_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"28a5b95820b6e9a1","mcp_get_code":{"code_sha256":"28a5b95820b6e9a1"}},{"arxiv_id":"2401.03082","paper":"/paper/umie-unified-multimodal-information","title":"UMIE: Unified Multimodal Information Extraction with Instruction Tuning","date":"2024-01-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ZUCC-AI/UMIE","path":"src/m2e2_clip_data.py","file_url":"https://github.com/ZUCC-AI/UMIE/blob/HEAD/src/m2e2_clip_data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"21200c59e1f98fb3","mcp_get_code":{"code_sha256":"21200c59e1f98fb3"}},{"arxiv_id":"2311.13385","paper":"/paper/segvol-universal-and-interactive-volumetric","title":"SegVol: Universal and Interactive Volumetric Medical Image Segmentation","date":"2023-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BAAI-DCAI/SegVol","path":"data_utils.py","file_url":"https://github.com/BAAI-DCAI/SegVol/blob/HEAD/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"883ac6b675c5850b","mcp_get_code":{"code_sha256":"883ac6b675c5850b"}},{"arxiv_id":"2309.00399","paper":"/paper/fine-grained-recognition-with-learnable","title":"Fine-grained Recognition with Learnable Semantic Data Augmentation","date":"2023-09-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/LearnableISDA","path":"utils/data_utils.py","file_url":"https://github.com/LeapLabTHU/LearnableISDA/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"51d41994183c270d","mcp_get_code":{"code_sha256":"51d41994183c270d"}},{"arxiv_id":"2308.14391","paper":"/paper/fire-food-image-to-recipe-generation","title":"FIRE: Food Image to REcipe generation","date":"2023-08-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"prateekchhikara/fire","path":"ingredients/data_loader.py","file_url":"https://github.com/prateekchhikara/fire/blob/HEAD/ingredients/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c1ac4e0ae35aedcf","mcp_get_code":{"code_sha256":"c1ac4e0ae35aedcf"}},{"arxiv_id":"2308.10467","paper":"/paper/single-user-injection-for-invisible-shilling","title":"Single-User Injection for Invisible Shilling Attack against Recommender Systems","date":"2023-08-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kdegroup/sui-attack","path":"data_pre.py","file_url":"https://github.com/kdegroup/sui-attack/blob/HEAD/data_pre.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2d5a913e453f8011","mcp_get_code":{"code_sha256":"2d5a913e453f8011"}},{"arxiv_id":"2307.15860","paper":"/paper/what-can-discriminator-do-towards-box-free","title":"What can Discriminator do? Towards Box-free Ownership Verification of Generative Adversarial Network","date":"2023-07-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"abstractteen/gan_ownership_verification","path":"data_loader.py","file_url":"https://github.com/abstractteen/gan_ownership_verification/blob/HEAD/data_loader.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9eb551a5d4ef16a1","mcp_get_code":{"code_sha256":"9eb551a5d4ef16a1"}},{"arxiv_id":"2301.07340","paper":"/paper/semi-supervised-semantic-segmentation-via-4","title":"Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant","date":"2023-01-18","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Jin-Ying/GTA-Seg","path":"gta/dataset/builder.py","file_url":"https://github.com/Jin-Ying/GTA-Seg/blob/HEAD/gta/dataset/builder.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":"85056ce7849ad6da","mcp_get_code":{"code_sha256":"85056ce7849ad6da"}},{"arxiv_id":"2211.02701","paper":"/paper/monai-an-open-source-framework-for-deep","title":"MONAI: An open-source framework for deep learning in healthcare","date":"2022-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaziciz/GLIMS","path":"utils/data_utils.py","file_url":"https://github.com/yaziciz/GLIMS/blob/HEAD/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4f0846ad1f425220","mcp_get_code":{"code_sha256":"4f0846ad1f425220"}},{"arxiv_id":"2208.10822","paper":"/paper/multimodal-across-domains-gaze-target","title":"Multimodal Across Domains Gaze Target Detection","date":"2022-08-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"francescotonini/multimodal-across-domains-gaze-target-detection","path":"datasets/build.py","file_url":"https://github.com/francescotonini/multimodal-across-domains-gaze-target-detection/blob/HEAD/datasets/build.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7635341f21c816ca","mcp_get_code":{"code_sha256":"7635341f21c816ca"}},{"arxiv_id":"2207.13362","paper":"/paper/camouflaged-object-detection-via-context","title":"Camouflaged Object Detection via Context-aware Cross-level Fusion","date":"2022-07-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thograce/C2FNet","path":"utils/dataloader.py","file_url":"https://github.com/thograce/C2FNet/blob/HEAD/utils/dataloader.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":"e04135543eeb06ab","mcp_get_code":{"code_sha256":"e04135543eeb06ab"}},{"arxiv_id":"2207.13048","paper":"/paper/domain-adaptation-under-open-set-label-shift","title":"Domain Adaptation under Open Set Label Shift","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/DANCE","path":"data_loader/get_loader.py","file_url":"https://github.com/VisionLearningGroup/DANCE/blob/HEAD/data_loader/get_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01528e3c98ec363f","mcp_get_code":{"code_sha256":"01528e3c98ec363f"}},{"arxiv_id":"2206.07737","paper":"/paper/disparate-impact-in-differential-privacy-from","title":"Disparate Impact in Differential Privacy from Gradient Misalignment","date":"2022-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"layer6ai-labs/fair-dp","path":"datasets/loaders.py","file_url":"https://github.com/layer6ai-labs/fair-dp/blob/HEAD/datasets/loaders.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":"6475a837db4b9ffa","mcp_get_code":{"code_sha256":"6475a837db4b9ffa"}},{"arxiv_id":"2204.02937","paper":"/paper/last-layer-re-training-is-sufficient-for","title":"Last Layer Re-Training is Sufficient for Robustness to Spurious Correlations","date":"2022-04-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PolinaKirichenko/deep_feature_reweighting","path":"wb_data.py","file_url":"https://github.com/PolinaKirichenko/deep_feature_reweighting/blob/HEAD/wb_data.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":"24cf9c3f2c0dee5f","mcp_get_code":{"code_sha256":"24cf9c3f2c0dee5f"}},{"arxiv_id":"2203.04570","paper":"/paper/cp-vit-cascade-vision-transformer-pruning-via","title":"CP-ViT: Cascade Vision Transformer Pruning via Progressive Sparsity Prediction","date":"2022-03-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ok858ok/CP-ViT","path":"CP-ViT/utils/data_utils.py","file_url":"https://github.com/ok858ok/CP-ViT/blob/HEAD/CP-ViT/utils/data_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3810fe8eaff038b2","mcp_get_code":{"code_sha256":"3810fe8eaff038b2"}},{"arxiv_id":"2112.07380","paper":"/paper/tracer-extreme-attention-guided-salient","title":"TRACER: Extreme Attention Guided Salient Object Tracing Network","date":"2021-12-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Karel911/TRACER","path":"dataloader.py","file_url":"https://github.com/Karel911/TRACER/blob/HEAD/dataloader.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":"48f924316939963f","mcp_get_code":{"code_sha256":"48f924316939963f"}},{"arxiv_id":"2110.08466","paper":"/paper/on-the-safety-of-conversational-models","title":"On the Safety of Conversational Models: Taxonomy, Dataset, and Benchmark","date":"2021-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"thu-coai/diasafety","path":"codes/mix_train.py","file_url":"https://github.com/thu-coai/diasafety/blob/HEAD/codes/mix_train.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":"3349be060ae9fe89","mcp_get_code":{"code_sha256":"3349be060ae9fe89"}},{"arxiv_id":"2110.08220","paper":"/paper/combining-diverse-feature-priors-1","title":"Combining Diverse Feature Priors","date":"2021-10-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MadryLab/copriors","path":"datasets/dataset_utils.py","file_url":"https://github.com/MadryLab/copriors/blob/HEAD/datasets/dataset_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"df7df2af74f39431","mcp_get_code":{"code_sha256":"df7df2af74f39431"}},{"arxiv_id":"2107.00860","paper":"/paper/rapid-neural-architecture-search-by-learning-1","title":"Rapid Neural Architecture Search by Learning to Generate Graphs from Datasets","date":"2021-07-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HayeonLee/MetaD2A","path":"MetaD2A_mobilenetV3/database/metaloader.py","file_url":"https://github.com/HayeonLee/MetaD2A/blob/HEAD/MetaD2A_mobilenetV3/database/metaloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"14ef56df31b716ed","mcp_get_code":{"code_sha256":"14ef56df31b716ed"}},{"arxiv_id":"2104.03344","paper":"/paper/ovanet-one-vs-all-network-for-universal","title":"OVANet: One-vs-All Network for Universal Domain Adaptation","date":"2021-04-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"VisionLearningGroup/OVANet","path":"data_loader/get_loader.py","file_url":"https://github.com/VisionLearningGroup/OVANet/blob/HEAD/data_loader/get_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8ff18904c899705c","mcp_get_code":{"code_sha256":"8ff18904c899705c"}},{"arxiv_id":"2010.05352","paper":"/paper/moco-pretraining-improves-representation-and","title":"MoCo-CXR: MoCo Pretraining Improves Representation and Transferability of Chest X-ray Models","date":"2020-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stanfordmlgroup/MedSelect","path":"src/moco/get_loader.py","file_url":"https://github.com/stanfordmlgroup/MedSelect/blob/HEAD/src/moco/get_loader.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":"55935351950f720b","mcp_get_code":{"code_sha256":"55935351950f720b"}},{"arxiv_id":"2010.02432","paper":"/paper/a-panda-no-it-s-a-sloth-slowdown-attacks-on-1","title":"A Panda? No, It's a Sloth: Slowdown Attacks on Adaptive Multi-Exit Neural Network Inference","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sanghyun-hong/deepsloth","path":"model_funcs.py","file_url":"https://github.com/sanghyun-hong/deepsloth/blob/HEAD/model_funcs.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2afaaf23c911b3e5","mcp_get_code":{"code_sha256":"2afaaf23c911b3e5"}},{"arxiv_id":"2009.14148","paper":"/paper/unbalanced-sobolev-descent","title":"Unbalanced Sobolev Descent","date":"2020-09-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IBM/USD","path":"unbalanced_descent.py","file_url":"https://github.com/IBM/USD/blob/HEAD/unbalanced_descent.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"1beaa0f96fbe0db6","mcp_get_code":{"code_sha256":"1beaa0f96fbe0db6"}},{"arxiv_id":"2008.04693","paper":"/paper/profit-a-novel-training-method-for-sub-4-bit","title":"PROFIT: A Novel Training Method for sub-4-bit MobileNet Models","date":"2020-08-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"EunhyeokPark/PROFIT","path":"my_lib/imagenet.py","file_url":"https://github.com/EunhyeokPark/PROFIT/blob/HEAD/my_lib/imagenet.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f7a778aac88a931","mcp_get_code":{"code_sha256":"1f7a778aac88a931"}},{"arxiv_id":"2007.05426","paper":"/paper/variational-inference-with-continuously","title":"Variational Inference with Continuously-Indexed Normalizing Flows","date":"2020-07-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anthonycaterini/cif-vi","path":"cif/datasets/loaders.py","file_url":"https://github.com/anthonycaterini/cif-vi/blob/HEAD/cif/datasets/loaders.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"51f878c58b289379","mcp_get_code":{"code_sha256":"51f878c58b289379"}},{"arxiv_id":"2007.02387","paper":"/paper/few-shot-relation-extraction-via-bayesian","title":"Few-shot Relation Extraction via Bayesian Meta-learning on Relation Graphs","date":"2020-07-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DeepGraphLearning/FewShotRE","path":"fewshot_re_kit/data_loader.py","file_url":"https://github.com/DeepGraphLearning/FewShotRE/blob/HEAD/fewshot_re_kit/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ce7216c1f8b98e4","mcp_get_code":{"code_sha256":"5ce7216c1f8b98e4"}},{"arxiv_id":"2004.11284","paper":"/paper/unsupervised-speech-decomposition-via-triple","title":"Unsupervised Speech Decomposition via Triple Information Bottleneck","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"auspicious3000/SpeechSplit","path":"data_loader.py","file_url":"https://github.com/auspicious3000/SpeechSplit/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"60e1b14d63143cce","mcp_get_code":{"code_sha256":"60e1b14d63143cce"}},{"arxiv_id":"2004.11284","paper":"/paper/unsupervised-speech-decomposition-via-triple","title":"Unsupervised Speech Decomposition via Triple Information Bottleneck","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"auspicious3000/autovc","path":"data_loader.py","file_url":"https://github.com/auspicious3000/autovc/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9190dd81722f0209","mcp_get_code":{"code_sha256":"9190dd81722f0209"}},{"arxiv_id":"2004.11284","paper":"/paper/unsupervised-speech-decomposition-via-triple","title":"Unsupervised Speech Decomposition via Triple Information Bottleneck","date":"2020-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tobwei/disentintel","path":"data_loader.py","file_url":"https://github.com/tobwei/disentintel/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a06b32f0a03e5e15","mcp_get_code":{"code_sha256":"a06b32f0a03e5e15"}},{"arxiv_id":"2003.03123","paper":"/paper/directional-message-passing-for-molecular-1","title":"Directional Message Passing for Molecular Graphs","date":"2020-03-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"akirasosa/pytorch-dimenet","path":"src/dimenet/loader.py","file_url":"https://github.com/akirasosa/pytorch-dimenet/blob/HEAD/src/dimenet/loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"13510a6058a2e6e3","mcp_get_code":{"code_sha256":"13510a6058a2e6e3"}},{"arxiv_id":"2002.10025","paper":"/paper/triple-wins-boosting-accuracy-robustness-and-1","title":"Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference","date":"2020-02-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gorakraj/earlyexit_onnx","path":"Networks/8. L2Stop/l2stop-master/sdn_stop/architectures/SDNs/ResNet_SDN.py","file_url":"https://github.com/gorakraj/earlyexit_onnx/blob/HEAD/Networks/8.%20L2Stop/l2stop-master/sdn_stop/architectures/SDNs/ResNet_SDN.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f5036fe874e2e4de","mcp_get_code":{"code_sha256":"f5036fe874e2e4de"}},{"arxiv_id":"2001.01568","paper":"/paper/learned-image-compression-with-discretized","title":"Learned Image Compression with Discretized Gaussian Mixture Likelihoods and Attention Modules","date":"2020-01-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention","path":"datasets.py","file_url":"https://github.com/LiuLei95/PyTorch-Learned-Image-Compression-with-GMM-and-Attention/blob/HEAD/datasets.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":"330dd4378c3e3a35","mcp_get_code":{"code_sha256":"330dd4378c3e3a35"}},{"arxiv_id":"1912.01865","paper":"/paper/stargan-v2-diverse-image-synthesis-for","title":"StarGAN v2: Diverse Image Synthesis for Multiple Domains","date":"2019-12-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"UdonDa/StarGAN-v2-pytorch-nonofficial","path":"data_loader.py","file_url":"https://github.com/UdonDa/StarGAN-v2-pytorch-nonofficial/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5ab14f9a150cfbc0","mcp_get_code":{"code_sha256":"5ab14f9a150cfbc0"}},{"arxiv_id":"1911.00888","paper":"/paper/multi-marginal-wasserstein-gan","title":"Multi-marginal Wasserstein GAN","date":"2019-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"basiralab/topoGAN","path":"data_loader.py","file_url":"https://github.com/basiralab/topoGAN/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ac96ef6fa350fee9","mcp_get_code":{"code_sha256":"ac96ef6fa350fee9"}},{"arxiv_id":"1909.09470","paper":"/paper/document-rectification-and-illumination","title":"Document Rectification and Illumination Correction using a Patch-based CNN","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoyu258/DocProj","path":"train_loader.py","file_url":"https://github.com/xiaoyu258/DocProj/blob/HEAD/train_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c15376b4b5eca830","mcp_get_code":{"code_sha256":"c15376b4b5eca830"}},{"arxiv_id":"1909.09470","paper":"/paper/document-rectification-and-illumination","title":"Document Rectification and Illumination Correction using a Patch-based CNN","date":"2019-09-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiaoyu258/DocProj","path":"train_loader_illumination.py","file_url":"https://github.com/xiaoyu258/DocProj/blob/HEAD/train_loader_illumination.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"604fa1896d3244e6","mcp_get_code":{"code_sha256":"604fa1896d3244e6"}},{"arxiv_id":"1906.06565","paper":"/paper/deep-set-prediction-networks","title":"Deep Set Prediction Networks","date":"2019-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Cyanogenoid/dspn","path":"dspn/data.py","file_url":"https://github.com/Cyanogenoid/dspn/blob/HEAD/dspn/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1868ee6802c6d911","mcp_get_code":{"code_sha256":"1868ee6802c6d911"}},{"arxiv_id":"1905.13736","paper":"/paper/unlabeled-data-improves-adversarial","title":"Unlabeled Data Improves Adversarial Robustness","date":"2019-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yguooo/semisup-adv","path":"dataloader.py","file_url":"https://github.com/yguooo/semisup-adv/blob/HEAD/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30546224f5287d93","mcp_get_code":{"code_sha256":"30546224f5287d93"}},{"arxiv_id":"1904.07399","paper":"/paper/adaptive-wing-loss-for-robust-face-alignment","title":"Adaptive Wing Loss for Robust Face Alignment via Heatmap Regression","date":"2019-04-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"affromero/SMILE","path":"data_loader.py","file_url":"https://github.com/affromero/SMILE/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2cef682f544273a6","mcp_get_code":{"code_sha256":"2cef682f544273a6"}},{"arxiv_id":"1805.00794","paper":"/paper/ecg-heartbeat-classification-a-deep","title":"ECG Heartbeat Classification: A Deep Transferable Representation","date":"2018-04-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Drajan/DDxNet","path":"utils/data_loader.py","file_url":"https://github.com/Drajan/DDxNet/blob/HEAD/utils/data_loader.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":"7c9871f6c1da69a6","mcp_get_code":{"code_sha256":"7c9871f6c1da69a6"}},{"arxiv_id":"1804.04732","paper":"/paper/multimodal-unsupervised-image-to-image","title":"Multimodal Unsupervised Image-to-Image Translation","date":"2018-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaxingwang/SDIT","path":"data_loader.py","file_url":"https://github.com/yaxingwang/SDIT/blob/HEAD/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"813e0e6fd4e0a9c4","mcp_get_code":{"code_sha256":"813e0e6fd4e0a9c4"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Spider-scnu/Instance-Segmentation-For-Cancer","path":"code/data_loader_cancer.py","file_url":"https://github.com/Spider-scnu/Instance-Segmentation-For-Cancer/blob/HEAD/code/data_loader_cancer.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":"31ef84f2073efaea","mcp_get_code":{"code_sha256":"31ef84f2073efaea"}},{"arxiv_id":"1804.03999","paper":"/paper/attention-u-net-learning-where-to-look-for","title":"Attention U-Net: Learning Where to Look for the Pancreas","date":"2018-04-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Spider-scnu/Instance-Segmentation-For-Cancer","path":"code/data_loader.py","file_url":"https://github.com/Spider-scnu/Instance-Segmentation-For-Cancer/blob/HEAD/code/data_loader.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":"5704356446f05667","mcp_get_code":{"code_sha256":"5704356446f05667"}},{"arxiv_id":"1612.06321","paper":"/paper/large-scale-image-retrieval-with-attentive","title":"Large-Scale Image Retrieval with Attentive Deep Local Features","date":"2016-12-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nashory/DeLF-pytorch","path":"train/dataloader.py","file_url":"https://github.com/nashory/DeLF-pytorch/blob/HEAD/train/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7a9dec8d9dfd2eb7","mcp_get_code":{"code_sha256":"7a9dec8d9dfd2eb7"}},{"arxiv_id":"1611.04849","paper":"/paper/deeply-supervised-salient-object-detection","title":"Deeply supervised salient object detection with short connections","date":"2016-11-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"AceCoooool/DSS-pytorch","path":"dataset.py","file_url":"https://github.com/AceCoooool/DSS-pytorch/blob/HEAD/dataset.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c30bbe8adf8ad847","mcp_get_code":{"code_sha256":"c30bbe8adf8ad847"}},{"arxiv_id":"1611.01704","paper":"/paper/end-to-end-optimized-image-compression","title":"End-to-end Optimized Image Compression","date":"2016-11-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liujiaheng/iclr_17_compression","path":"datasets.py","file_url":"https://github.com/liujiaheng/iclr_17_compression/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"330dd4378c3e3a35","mcp_get_code":{"code_sha256":"330dd4378c3e3a35"}},{"arxiv_id":"1508.06576","paper":"/paper/a-neural-algorithm-of-artistic-style","title":"A Neural Algorithm of Artistic Style","date":"2015-08-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jtiger958/style-transfer-pytorch","path":"dataloader/dataloader.py","file_url":"https://github.com/jtiger958/style-transfer-pytorch/blob/HEAD/dataloader/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bb819e37f3028e3b","mcp_get_code":{"code_sha256":"bb819e37f3028e3b"}},{"arxiv_id":"1411.1784","paper":"/paper/conditional-generative-adversarial-nets","title":"Conditional Generative Adversarial Nets","date":"2014-11-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"doansangg/CGAN-PyTorch","path":"dataloader.py","file_url":"https://github.com/doansangg/CGAN-PyTorch/blob/HEAD/dataloader.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":"20278399f273f79e","mcp_get_code":{"code_sha256":"20278399f273f79e"}},{"arxiv_id":"aaai_28521","paper":null,"title":"arXiv:aaai_28521","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"winter-flow/WSMD","path":"data.py","file_url":"https://github.com/winter-flow/WSMD/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"258911cfac89ec47","mcp_get_code":{"code_sha256":"258911cfac89ec47"}},{"arxiv_id":"aaai_28521","paper":null,"title":"arXiv:aaai_28521","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"winter-flow/WSMD","path":"dataloader.py","file_url":"https://github.com/winter-flow/WSMD/blob/HEAD/dataloader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"bc3b2391b7cb324c","mcp_get_code":{"code_sha256":"bc3b2391b7cb324c"}},{"arxiv_id":"aaai_25958","paper":null,"title":"arXiv:aaai_25958","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"tsingqguo/bgmix","path":"train_Classifier.py","file_url":"https://github.com/tsingqguo/bgmix/blob/HEAD/train_Classifier.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"e611aa6b0a184d4f","mcp_get_code":{"code_sha256":"e611aa6b0a184d4f"}},{"arxiv_id":"Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper","paper":null,"title":"arXiv:Wang_Hunting_Sparsity_Density-Guided_Contrastive_Learning_for_Semi-Supervised_Semantic_Segmentation_CVPR_2023_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"Gavinwxy/DGCL","path":"dgcl/dataset/builder.py","file_url":"https://github.com/Gavinwxy/DGCL/blob/HEAD/dgcl/dataset/builder.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":"fa5a641bc14640ed","mcp_get_code":{"code_sha256":"fa5a641bc14640ed"}},{"arxiv_id":"2023.findings-emnlp.153","paper":null,"title":"arXiv:2023.findings-emnlp.153","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"thunlp/FewRel","path":"fewshot_re_kit/data_loader.py","file_url":"https://github.com/thunlp/FewRel/blob/HEAD/fewshot_re_kit/data_loader.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"000548a22f0525ba","mcp_get_code":{"code_sha256":"000548a22f0525ba"}},{"arxiv_id":"2023.acl-long.452","paper":null,"title":"arXiv:2023.acl-long.452","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"terarachang/DataICL","path":"train_datamodels.py","file_url":"https://github.com/terarachang/DataICL/blob/HEAD/train_datamodels.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b7f1b985949e4e6e","mcp_get_code":{"code_sha256":"b7f1b985949e4e6e"}}]}