{"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/downsample-2","entry":"Downsample","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":58,"n_papers_ran":51,"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":66,"n_samples_ran":58,"n_samples_fingerprinted":39,"n_places":66,"n_places_pointer_only":36,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":3,"ran_fixture":0,"ran":55,"unverified":8},"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":"2601.19498","paper":"/paper/arxiv-2601-19498","title":"Cortex-Grounded Diffusion Models for Brain Image Generation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ai-med/Cor2Vox","path":"model/BrownianBridge/BrownianBridgeModel_c2v.py","file_url":"https://github.com/ai-med/Cor2Vox/blob/HEAD/model/BrownianBridge/BrownianBridgeModel_c2v.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":"d834a01c15616803","mcp_get_code":{"code_sha256":"d834a01c15616803"}},{"arxiv_id":"2601.17470","paper":"/paper/arxiv-2601-17470","title":"PhaSR: Generalized Image Shadow Removal with Physically Aligned Priors","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"ming053l/PhaSR","path":"model.py","file_url":"https://github.com/ming053l/PhaSR/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7394455830519eb7","mcp_get_code":{"code_sha256":"7394455830519eb7"}},{"arxiv_id":"2601.07692","paper":"/paper/arxiv-2601-07692","title":"R3DPA: Leveraging 3D Representation Alignment and RGB Pretrained Priors for LiDAR Scene Generation","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"valeoai/R3DPA","path":"models/model_lidm.py","file_url":"https://github.com/valeoai/R3DPA/blob/HEAD/models/model_lidm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"16b28faf22dd40f3","mcp_get_code":{"code_sha256":"16b28faf22dd40f3"}},{"arxiv_id":"2601.05212","paper":"/paper/arxiv-2601-05212","title":"FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"sisinflab/FlowLet","path":"flowlet/models/flow_matching.py","file_url":"https://github.com/sisinflab/FlowLet/blob/HEAD/flowlet/models/flow_matching.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9221a7ceaef441e6","mcp_get_code":{"code_sha256":"9221a7ceaef441e6"}},{"arxiv_id":"2601.00328","paper":"/paper/arxiv-2601-00328","title":"Joint Geometry-Appearance Human Reconstruction in a Unified Latent Space via Bridge Diffusion","date":null,"month_inferred_from_arxiv_id":"2026-01","title_source":"syntology","repo":"haiantyz/JGA-LBD","path":"JGA-LBD/DDBM/ddbm/unet3d.py","file_url":"https://github.com/haiantyz/JGA-LBD/blob/HEAD/JGA-LBD/DDBM/ddbm/unet3d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5026329def564f79","mcp_get_code":{"code_sha256":"5026329def564f79"}},{"arxiv_id":"2509.02101","paper":"/paper/arxiv-2509-02101","title":"-Semantics-Aware Logical Anomaly Detection","date":null,"month_inferred_from_arxiv_id":"2025-09","title_source":"syntology","repo":"MaticFuc/SALAD","path":"ae.py","file_url":"https://github.com/MaticFuc/SALAD/blob/HEAD/ae.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"702a4dec7df1f104","mcp_get_code":{"code_sha256":"702a4dec7df1f104"}},{"arxiv_id":"2508.03256","paper":"/paper/arxiv-2508-03256","title":"Beyond Isolated Words: Diffusion Brush for Handwritten Text-Line Generation","date":null,"month_inferred_from_arxiv_id":"2025-08","title_source":"syntology","repo":"dailenson/DiffBrush","path":"models/unet.py","file_url":"https://github.com/dailenson/DiffBrush/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"763eaf30a7ec2801","mcp_get_code":{"code_sha256":"763eaf30a7ec2801"}},{"arxiv_id":"2505.17685","paper":"/paper/futuresightdrive-thinking-visually-with","title":"FutureSightDrive: Thinking Visually with Spatio-Temporal CoT for Autonomous Driving","date":"2025-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MIV-XJTU/FSDrive","path":"MoVQGAN/movqgan/models/vqgan.py","file_url":"https://github.com/MIV-XJTU/FSDrive/blob/HEAD/MoVQGAN/movqgan/models/vqgan.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"37124eedd502424a","mcp_get_code":{"code_sha256":"37124eedd502424a"}},{"arxiv_id":"2505.17022","paper":"/paper/got-r1-unleashing-reasoning-capability-of","title":"GoT-R1: Unleashing Reasoning Capability of MLLM for Visual Generation with Reinforcement Learning","date":"2025-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"gogoduan/got-r1","path":"src/models/vq_model.py","file_url":"https://github.com/gogoduan/got-r1/blob/HEAD/src/models/vq_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6618daf64230cf0c","mcp_get_code":{"code_sha256":"6618daf64230cf0c"}},{"arxiv_id":"2505.07071","paper":"/paper/semantic-guided-diffusion-model-for-single","title":"Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution","date":"2025-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Liu-Zihang/SAMSR","path":"models/unet.py","file_url":"https://github.com/Liu-Zihang/SAMSR/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5a923f8114a667b6","mcp_get_code":{"code_sha256":"5a923f8114a667b6"}},{"arxiv_id":"2504.15371","paper":"/paper/event2vec-processing-neuromorphic-events","title":"Event2Vec: Processing neuromorphic events directly by representations in vector space","date":"2025-04-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fangwei123456/event2vec","path":"models.py","file_url":"https://github.com/fangwei123456/event2vec/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"79a52e32e4bca542","mcp_get_code":{"code_sha256":"79a52e32e4bca542"}},{"arxiv_id":"2504.02160","paper":"/paper/less-to-more-generalization-unlocking-more","title":"Less-to-More Generalization: Unlocking More Controllability by In-Context Generation","date":"2025-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bytedance/UNO","path":"uno/flux/pipeline.py","file_url":"https://github.com/bytedance/UNO/blob/HEAD/uno/flux/pipeline.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"e50b194d9e5c1fb2","mcp_get_code":{"code_sha256":"e50b194d9e5c1fb2"}},{"arxiv_id":"2502.16025","paper":"/paper/featsharp-your-vision-model-features-sharper","title":"FeatSharp: Your Vision Model Features, Sharper","date":"2025-02-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nvlabs/radio","path":"radio/eradio_model.py","file_url":"https://github.com/nvlabs/radio/blob/HEAD/radio/eradio_model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"c22f7b3faad1fe11","mcp_get_code":{"code_sha256":"c22f7b3faad1fe11"}},{"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":"527a7a3a5a9a53a1","mcp_get_code":{"code_sha256":"527a7a3a5a9a53a1"}},{"arxiv_id":"2407.21705","paper":"/paper/tora-trajectory-oriented-diffusion","title":"Tora: Trajectory-oriented Diffusion Transformer for Video Generation","date":"2024-07-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alibaba/Tora","path":"diffusers-version/tora/transformer_3d.py","file_url":"https://github.com/alibaba/Tora/blob/HEAD/diffusers-version/tora/transformer_3d.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":"6a1cf7ce76caca59","mcp_get_code":{"code_sha256":"6a1cf7ce76caca59"}},{"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_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b5bf8353f4199efb","mcp_get_code":{"code_sha256":"b5bf8353f4199efb"}},{"arxiv_id":"2407.13987","paper":"/paper/realviformer-investigating-attention-for-real","title":"RealViformer: Investigating Attention for Real-World Video Super-Resolution","date":"2024-07-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Yuehan717/RealViformer","path":"archs/realviformer_arch.py","file_url":"https://github.com/Yuehan717/RealViformer/blob/HEAD/archs/realviformer_arch.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4bde66e2e3722ca8","mcp_get_code":{"code_sha256":"4bde66e2e3722ca8"}},{"arxiv_id":"2407.10172","paper":"/paper/restoring-images-in-adverse-weather","title":"Restoring Images in Adverse Weather Conditions via Histogram Transformer","date":"2024-07-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sunshangquan/Histoformer","path":"basicsr/models/archs/histoformer_arch.py","file_url":"https://github.com/sunshangquan/Histoformer/blob/HEAD/basicsr/models/archs/histoformer_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8728564ca09fb5d2","mcp_get_code":{"code_sha256":"8728564ca09fb5d2"}},{"arxiv_id":"2407.05232","paper":"/paper/papm-a-physics-aware-proxy-model-for-process","title":"PAPM: A Physics-aware Proxy Model for Process Systems","date":"2024-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"pengwei07/papm","path":"papm/model/pdeunet.py","file_url":"https://github.com/pengwei07/papm/blob/HEAD/papm/model/pdeunet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"96a09a7a1458f6da","mcp_get_code":{"code_sha256":"96a09a7a1458f6da"}},{"arxiv_id":"2406.08473","paper":"/paper/strategies-for-pretraining-neural-operators","title":"Strategies for Pretraining Neural Operators","date":"2024-06-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"anthonyzhou-1/pretraining_pdes","path":"models/unet.py","file_url":"https://github.com/anthonyzhou-1/pretraining_pdes/blob/HEAD/models/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c6f83aa983db012","mcp_get_code":{"code_sha256":"2c6f83aa983db012"}},{"arxiv_id":"2404.14743","paper":"/paper/gradient-guidance-for-diffusion-models-an","title":"Gradient Guidance for Diffusion Models: An Optimization Perspective","date":"2024-04-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yukang123/ggdmoptim","path":"simulations/unet_1d.py","file_url":"https://github.com/yukang123/ggdmoptim/blob/HEAD/simulations/unet_1d.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7247cda50fed9ce5","mcp_get_code":{"code_sha256":"7247cda50fed9ce5"}},{"arxiv_id":"2404.01547","paper":"/paper/bidirectional-multi-scale-implicit-neural","title":"Bidirectional Multi-Scale Implicit Neural Representations for Image Deraining","date":"2024-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"cschenxiang/NeRD-Rain","path":"model.py","file_url":"https://github.com/cschenxiang/NeRD-Rain/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":"400a7978bb40b09a","mcp_get_code":{"code_sha256":"400a7978bb40b09a"}},{"arxiv_id":"2404.00288","paper":null,"title":"arXiv:2404.00288","date":null,"month_inferred_from_arxiv_id":"2024-04","title_source":null,"repo":"joshyZhou/FPro","path":"basicsr/models/archs/FPro_arch.py","file_url":"https://github.com/joshyZhou/FPro/blob/HEAD/basicsr/models/archs/FPro_arch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"43cd6fa3751bab78","mcp_get_code":{"code_sha256":"43cd6fa3751bab78"}},{"arxiv_id":"2404.00815","paper":"/paper/towards-realistic-scene-generation-with-lidar","title":"Towards Realistic Scene Generation with LiDAR Diffusion Models","date":"2024-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hancyran/lidar-diffusion","path":"lidm/modules/diffusion/model_lidm.py","file_url":"https://github.com/hancyran/lidar-diffusion/blob/HEAD/lidm/modules/diffusion/model_lidm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ba09c4650052a805","mcp_get_code":{"code_sha256":"ba09c4650052a805"}},{"arxiv_id":"2403.12580","paper":"/paper/real-iad-a-real-world-multi-view-dataset-for","title":"Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection","date":"2024-03-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhangzjn/ader","path":"model/realnet.py","file_url":"https://github.com/zhangzjn/ader/blob/HEAD/model/realnet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"6e2380afa896c38d","mcp_get_code":{"code_sha256":"6e2380afa896c38d"}},{"arxiv_id":"2403.10897","paper":"/paper/rethinking-multi-view-representation-learning","title":"Rethinking Multi-view Representation Learning via Distilled Disentangling","date":"2024-03-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Guanzhou-Ke/MRDD","path":"src/models/mrdd.py","file_url":"https://github.com/Guanzhou-Ke/MRDD/blob/HEAD/src/models/mrdd.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b394ee852c6ff0c5","mcp_get_code":{"code_sha256":"b394ee852c6ff0c5"}},{"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":"0ec25aa4e42d8883","mcp_get_code":{"code_sha256":"0ec25aa4e42d8883"}},{"arxiv_id":"2402.13040","paper":"/paper/text-guided-molecule-generation-with","title":"Text-Guided Molecule Generation with Diffusion Language Model","date":"2024-02-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Deno-V/tgm-dlm","path":"improved-diffusion/improved_diffusion/transformer_model.py","file_url":"https://github.com/Deno-V/tgm-dlm/blob/HEAD/improved-diffusion/improved_diffusion/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":"6217144fde18c480","mcp_get_code":{"code_sha256":"6217144fde18c480"}},{"arxiv_id":"2310.10088","paper":"/paper/puca-patch-unshuffle-and-channel-attention-1","title":"PUCA: Patch-Unshuffle and Channel Attention for Enhanced Self-Supervised Image Denoising","date":"2023-10-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HyemiEsme/PUCA","path":"src/model/PUCA.py","file_url":"https://github.com/HyemiEsme/PUCA/blob/HEAD/src/model/PUCA.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"18598f48ae5f2314","mcp_get_code":{"code_sha256":"18598f48ae5f2314"}},{"arxiv_id":"2310.03559","paper":"/paper/medsynv1-text-guided-anatomy-aware-synthesis","title":"MedSyn: Text-guided Anatomy-aware Synthesis of High-Fidelity 3D CT Images","date":"2023-10-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"batmanlab/medsyn","path":"src/train_low_res.py","file_url":"https://github.com/batmanlab/medsyn/blob/HEAD/src/train_low_res.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"ae694c2c90f4aa81","mcp_get_code":{"code_sha256":"ae694c2c90f4aa81"}},{"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":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"8c5676227adb2492","mcp_get_code":{"code_sha256":"8c5676227adb2492"}},{"arxiv_id":"2309.02020","paper":"/paper/rawhdr-high-dynamic-range-image","title":"RawHDR: High Dynamic Range Image Reconstruction from a Single Raw Image","date":"2023-09-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jackzou233/RawHDR","path":"model.py","file_url":"https://github.com/jackzou233/RawHDR/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5b78062820f7c165","mcp_get_code":{"code_sha256":"5b78062820f7c165"}},{"arxiv_id":"2308.14036","paper":"/paper/mb-taylorformer-multi-branch-efficient","title":"MB-TaylorFormer: Multi-branch Efficient Transformer Expanded by Taylor Formula for Image Dehazing","date":"2023-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"FVL2020/ICCV-2023-MB-TaylorFormer","path":"basicsr/models/archs/MB_TaylorFormer.py","file_url":"https://github.com/FVL2020/ICCV-2023-MB-TaylorFormer/blob/HEAD/basicsr/models/archs/MB_TaylorFormer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"428e6e039f598efa","mcp_get_code":{"code_sha256":"428e6e039f598efa"}},{"arxiv_id":"2307.14010","paper":"/paper/essaformer-efficient-transformer-for","title":"ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution","date":"2023-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rexzhan/essaformer","path":"ESSA.py","file_url":"https://github.com/rexzhan/essaformer/blob/HEAD/ESSA.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Unlicense","inline_ok":true,"code_sha256_prefix":"4dd28ee316a86d05","mcp_get_code":{"code_sha256":"4dd28ee316a86d05"}},{"arxiv_id":"2307.01952","paper":"/paper/sdxl-improving-latent-diffusion-models-for","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"compvis/fm-boosting","path":"fmboost/models/unet/model.py","file_url":"https://github.com/compvis/fm-boosting/blob/HEAD/fmboost/models/unet/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"03cbae4dc00dbd28","mcp_get_code":{"code_sha256":"03cbae4dc00dbd28"}},{"arxiv_id":"2307.01952","paper":"/paper/sdxl-improving-latent-diffusion-models-for","title":"SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis","date":"2023-07-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"stability-ai/generative-models","path":"sgm/modules/diffusionmodules/openaimodel.py","file_url":"https://github.com/stability-ai/generative-models/blob/HEAD/sgm/modules/diffusionmodules/openaimodel.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"3ee1e71fc45df15b","mcp_get_code":{"code_sha256":"3ee1e71fc45df15b"}},{"arxiv_id":"2305.16283","paper":"/paper/commonscenes-generating-commonsense-3d-indoor","title":"CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph Diffusion","date":"2023-05-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ymxlzgy/commonscenes","path":"model/networks/diffusion_networks/sg_diff.py","file_url":"https://github.com/ymxlzgy/commonscenes/blob/HEAD/model/networks/diffusion_networks/sg_diff.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"d30cfba3625bdc96","mcp_get_code":{"code_sha256":"d30cfba3625bdc96"}},{"arxiv_id":"2303.09826","paper":"/paper/learning-data-driven-vector-quantized","title":"Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution","date":"2023-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"researchmm/VQD-SR","path":"vqgan/model_multiscale_load_top.py","file_url":"https://github.com/researchmm/VQD-SR/blob/HEAD/vqgan/model_multiscale_load_top.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"db6df78810faee4a","mcp_get_code":{"code_sha256":"db6df78810faee4a"}},{"arxiv_id":"2303.05754","paper":"/paper/fast-diffusion-sampler-for-inverse-problems","title":"Decomposed Diffusion Sampler for Accelerating Large-Scale Inverse Problems","date":"2023-03-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hj-harry/dds","path":"solver_2d.py","file_url":"https://github.com/hj-harry/dds/blob/HEAD/solver_2d.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"72f641bded2a58d5","mcp_get_code":{"code_sha256":"72f641bded2a58d5"}},{"arxiv_id":"2301.06468","paper":"/paper/msanii-high-fidelity-music-synthesis-on-a","title":"Msanii: High Fidelity Music Synthesis on a Shoestring Budget","date":"2023-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kinyugo/msanii","path":"msanii/modules/modules.py","file_url":"https://github.com/kinyugo/msanii/blob/HEAD/msanii/modules/modules.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"959bae2750dbabbf","mcp_get_code":{"code_sha256":"959bae2750dbabbf"}},{"arxiv_id":"2301.05225","paper":"/paper/domain-expansion-of-image-generators","title":"Domain Expansion of Image Generators","date":"2023-01-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lllyasviel/controlnet","path":"cldm/cldm.py","file_url":"https://github.com/lllyasviel/controlnet/blob/HEAD/cldm/cldm.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":"0feab49c606075bb","mcp_get_code":{"code_sha256":"0feab49c606075bb"}},{"arxiv_id":"2211.02048","paper":"/paper/efficient-spatially-sparse-inference-for","title":"Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models","date":"2022-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lmxyy/sige","path":"diffusion/models/ddpm_arch/sige_fused_unet.py","file_url":"https://github.com/lmxyy/sige/blob/HEAD/diffusion/models/ddpm_arch/sige_fused_unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"7a0f92aace8379bd","mcp_get_code":{"code_sha256":"7a0f92aace8379bd"}},{"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":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"e8df5d8fcd9e9339","mcp_get_code":{"code_sha256":"e8df5d8fcd9e9339"}},{"arxiv_id":"2207.12598","paper":"/paper/classifier-free-diffusion-guidance","title":"Classifier-Free Diffusion Guidance","date":"2022-07-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"g4vrel/DDPM","path":"sample.py","file_url":"https://github.com/g4vrel/DDPM/blob/HEAD/sample.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2cdc7e0af049b4ea","mcp_get_code":{"code_sha256":"2cdc7e0af049b4ea"}},{"arxiv_id":"2207.09840","paper":"/paper/elegant-exquisite-and-locally-editable-gan","title":"EleGANt: Exquisite and Locally Editable GAN for Makeup Transfer","date":"2022-07-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Chenyu-Yang-2000/EleGANt","path":"models/elegant.py","file_url":"https://github.com/Chenyu-Yang-2000/EleGANt/blob/HEAD/models/elegant.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"24e2b7616e3ae391","mcp_get_code":{"code_sha256":"24e2b7616e3ae391"}},{"arxiv_id":"2205.12021","paper":"/paper/patchnr-learning-from-small-data-by-patch","title":"PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization","date":"2022-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fabianaltekrueger/patchnr","path":"patchNR_zeroshot.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"aed48d89e94f836b","mcp_get_code":{"code_sha256":"aed48d89e94f836b"}},{"arxiv_id":"2205.12021","paper":"/paper/patchnr-learning-from-small-data-by-patch","title":"PatchNR: Learning from Very Few Images by Patch Normalizing Flow Regularization","date":"2022-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"fabianaltekrueger/patchnr","path":"patchNR_zeroshot_material.py","file_url":"https://github.com/fabianaltekrueger/patchnr/blob/HEAD/patchNR_zeroshot_material.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ad0580d02dd64705","mcp_get_code":{"code_sha256":"ad0580d02dd64705"}},{"arxiv_id":"2205.11487","paper":"/paper/photorealistic-text-to-image-diffusion-models","title":"Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding","date":"2022-05-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deep-floyd/if","path":"deepfloyd_if/model/unet.py","file_url":"https://github.com/deep-floyd/if/blob/HEAD/deepfloyd_if/model/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"93058e5e24fa4d4a","mcp_get_code":{"code_sha256":"93058e5e24fa4d4a"}},{"arxiv_id":"2204.13656","paper":"/paper/unsupervised-multi-modal-medical-image","title":"Unsupervised Multi-Modal Medical Image Registration via Discriminator-Free Image-to-Image Translation","date":"2022-04-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"heyblackC/DFMIR","path":"models/networks.py","file_url":"https://github.com/heyblackC/DFMIR/blob/HEAD/models/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7f3677d806b3c27d","mcp_get_code":{"code_sha256":"7f3677d806b3c27d"}},{"arxiv_id":"2204.00993","paper":"/paper/improving-vision-transformers-by-revisiting","title":"Improving Vision Transformers by Revisiting High-frequency Components","date":"2022-04-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jiawangbai/HAT","path":"models/volo.py","file_url":"https://github.com/jiawangbai/HAT/blob/HEAD/models/volo.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"33ecaec51f88c518","mcp_get_code":{"code_sha256":"33ecaec51f88c518"}},{"arxiv_id":"2203.14186","paper":"/paper/rstt-real-time-spatial-temporal-transformer","title":"RSTT: Real-time Spatial Temporal Transformer for Space-Time Video Super-Resolution","date":"2022-03-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"llmpass/RSTT","path":"models/RSTT.py","file_url":"https://github.com/llmpass/RSTT/blob/HEAD/models/RSTT.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"190b1325503c4ab8","mcp_get_code":{"code_sha256":"190b1325503c4ab8"}},{"arxiv_id":"2203.09516","paper":"/paper/autosdf-shape-priors-for-3d-completion","title":"AutoSDF: Shape Priors for 3D Completion, Reconstruction and Generation","date":"2022-03-17","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yccyenchicheng/AutoSDF","path":"models/networks/pvqvae_networks/auto_encoder.py","file_url":"https://github.com/yccyenchicheng/AutoSDF/blob/HEAD/models/networks/pvqvae_networks/auto_encoder.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"bdf8f3f0628b16b9","mcp_get_code":{"code_sha256":"bdf8f3f0628b16b9"}},{"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":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"cfe765a405ec911f","mcp_get_code":{"code_sha256":"cfe765a405ec911f"}},{"arxiv_id":"2111.03042","paper":"/paper/unsupervised-learning-of-compositional-energy","title":"Unsupervised Learning of Compositional Energy Concepts","date":"2021-11-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yilundu/comet","path":"models.py","file_url":"https://github.com/yilundu/comet/blob/HEAD/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"15c654593131627f","mcp_get_code":{"code_sha256":"15c654593131627f"}},{"arxiv_id":"2107.11298","paper":"/paper/surfacenet-adversarial-svbrdf-estimation-from","title":"SurfaceNet: Adversarial SVBRDF Estimation from a Single Image","date":"2021-07-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"perceivelab/trf-sg2im","path":"modules/blocks.py","file_url":"https://github.com/perceivelab/trf-sg2im/blob/HEAD/modules/blocks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"GPL-3.0","inline_ok":false,"code_sha256_prefix":"f86ae8958e076367","mcp_get_code":{"code_sha256":"f86ae8958e076367"}},{"arxiv_id":"2106.02689","paper":"/paper/regionvit-regional-to-local-attention-for","title":"RegionViT: Regional-to-Local Attention for Vision Transformers","date":"2021-06-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"conceptofmind/Region-ViT-flax","path":"region_vit_flax.py","file_url":"https://github.com/conceptofmind/Region-ViT-flax/blob/HEAD/region_vit_flax.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ddaf1aef43c3e9f2","mcp_get_code":{"code_sha256":"ddaf1aef43c3e9f2"}},{"arxiv_id":"2105.05233","paper":"/paper/diffusion-models-beat-gans-on-image-synthesis","title":"Diffusion Models Beat GANs on Image Synthesis","date":"2021-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"clu0/unet.cu","path":"dev/unet.py","file_url":"https://github.com/clu0/unet.cu/blob/HEAD/dev/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2b4dc2218b2a8487","mcp_get_code":{"code_sha256":"2b4dc2218b2a8487"}},{"arxiv_id":"2105.05233","paper":"/paper/diffusion-models-beat-gans-on-image-synthesis","title":"Diffusion Models Beat GANs on Image Synthesis","date":"2021-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Janspiry/Palette-Image-to-Image-Diffusion-Models","path":"models/guided_diffusion_modules/unet.py","file_url":"https://github.com/Janspiry/Palette-Image-to-Image-Diffusion-Models/blob/HEAD/models/guided_diffusion_modules/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"98fe2137d5940de7","mcp_get_code":{"code_sha256":"98fe2137d5940de7"}},{"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":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b1f2e26fa3f1c03a","mcp_get_code":{"code_sha256":"b1f2e26fa3f1c03a"}},{"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":"Ashwin-Pokharel/base_diffusion","path":"model.py","file_url":"https://github.com/Ashwin-Pokharel/base_diffusion/blob/HEAD/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"621e4b433c363624","mcp_get_code":{"code_sha256":"621e4b433c363624"}},{"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":"yiyixuxu/denoising-diffusion-flax","path":"denoising_diffusion_flax/unet.py","file_url":"https://github.com/yiyixuxu/denoising-diffusion-flax/blob/HEAD/denoising_diffusion_flax/unet.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":"9e2c1a5763e42fb3","mcp_get_code":{"code_sha256":"9e2c1a5763e42fb3"}},{"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_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f5c52234eed97ee9","mcp_get_code":{"code_sha256":"f5c52234eed97ee9"}},{"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":"andreasfloros/diffusion","path":"improved_diffusion.py","file_url":"https://github.com/andreasfloros/diffusion/blob/HEAD/improved_diffusion.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"30c3da436cee282c","mcp_get_code":{"code_sha256":"30c3da436cee282c"}},{"arxiv_id":"2006.05749","paper":"/paper/interpolation-between-residual-and-non","title":"Interpolation between Residual and Non-Residual Networks","date":"2020-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"minicheshire/InResNet","path":"InResNet.py","file_url":"https://github.com/minicheshire/InResNet/blob/HEAD/InResNet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0704c0c7cedba3e0","mcp_get_code":{"code_sha256":"0704c0c7cedba3e0"}},{"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":"sk1123344/U-2-NET-and-U-NET","path":"unet.py","file_url":"https://github.com/sk1123344/U-2-NET-and-U-NET/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":"13052d26dd453e49","mcp_get_code":{"code_sha256":"13052d26dd453e49"}},{"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":"labmlai/annotated_deep_learning_paper_implementations","path":"labml_nn/diffusion/ddpm/unet.py","file_url":"https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/HEAD/labml_nn/diffusion/ddpm/unet.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"90003de3f34414b2","mcp_get_code":{"code_sha256":"90003de3f34414b2"}}]}