{"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/dict-to-nonedict","entry":"dict_to_nonedict","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":22,"n_papers_ran":20,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":22,"n_places_pointer_only":5,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":1},"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":"2406.18516","paper":"/paper/denoising-as-adaptation-noise-space-domain","title":"Denoising as Adaptation: Noise-Space Domain Adaptation for Image Restoration","date":"2024-06-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kangliao929/noise-da","path":"core/praser.py","file_url":"https://github.com/kangliao929/noise-da/blob/HEAD/core/praser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"38e77ca35f1f8bcb","mcp_get_code":{"code_sha256":"38e77ca35f1f8bcb"}},{"arxiv_id":"2404.09732","paper":"/paper/photo-realistic-image-restoration-in-the-wild","title":"Photo-Realistic Image Restoration in the Wild with Controlled Vision-Language Models","date":"2024-04-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"algolzw/daclip-uir","path":"universal-image-restoration/config/daclip-sde/options.py","file_url":"https://github.com/algolzw/daclip-uir/blob/HEAD/universal-image-restoration/config/daclip-sde/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2403.19898","paper":"/paper/structure-matters-tackling-the-semantic","title":"Structure Matters: Tackling the Semantic Discrepancy in Diffusion Models for Image Inpainting","date":"2024-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"htyjers/StrDiffusion","path":"train/discriminator/config/inpainting/options.py","file_url":"https://github.com/htyjers/StrDiffusion/blob/HEAD/train/discriminator/config/inpainting/options.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2403.01497","paper":"/paper/learning-a-physical-aware-diffusion-model","title":"Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement","date":"2024-03-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenydong/pa-diff","path":"core/logger.py","file_url":"https://github.com/chenydong/pa-diff/blob/HEAD/core/logger.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2312.11121","paper":"/paper/multi-scale-reconstruction-of-turbulent","title":"Multi-scale Reconstruction of Turbulent Rotating Flows with Generative Diffusion Models","date":null,"month_inferred_from_arxiv_id":"2023-12","title_source":"archive","repo":"smartturb/palette-turb","path":"core/praser.py","file_url":"https://github.com/smartturb/palette-turb/blob/HEAD/core/praser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38e77ca35f1f8bcb","mcp_get_code":{"code_sha256":"38e77ca35f1f8bcb"}},{"arxiv_id":"2312.00210","paper":"/paper/dream-diffusion-rectification-and-estimation","title":"DREAM: Diffusion Rectification and Estimation-Adaptive Models","date":"2023-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jinxinzhou/dream","path":"sr3/core/logger.py","file_url":"https://github.com/jinxinzhou/dream/blob/HEAD/sr3/core/logger.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2310.12848","paper":"/paper/neural-degradation-representation-learning","title":"Neural Degradation Representation Learning for All-In-One Image Restoration","date":"2023-10-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mdyao/NDR-Restore","path":"options/options.py","file_url":"https://github.com/mdyao/NDR-Restore/blob/HEAD/options/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2310.07492","paper":"/paper/boosting-black-box-attack-to-deep-neural","title":"Boosting Black-box Attack to Deep Neural Networks with Conditional Diffusion Models","date":"2023-10-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ryliu68/CDMA","path":"core/praser.py","file_url":"https://github.com/ryliu68/CDMA/blob/HEAD/core/praser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"38e77ca35f1f8bcb","mcp_get_code":{"code_sha256":"38e77ca35f1f8bcb"}},{"arxiv_id":"2310.01018","paper":"/paper/controlling-vision-language-models-for","title":"Controlling Vision-Language Models for Multi-Task Image Restoration","date":"2023-10-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Algolzw/daclip-uir","path":"universal-image-restoration/config/daclip-sde/options.py","file_url":"https://github.com/Algolzw/daclip-uir/blob/HEAD/universal-image-restoration/config/daclip-sde/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2308.04417","paper":"/paper/diffcr-a-fast-conditional-diffusion-framework","title":"DiffCR: A Fast Conditional Diffusion Framework for Cloud Removal from Optical Satellite Images","date":"2023-08-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xavierjiezou/diffcr","path":"core/praser.py","file_url":"https://github.com/xavierjiezou/diffcr/blob/HEAD/core/praser.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"38e77ca35f1f8bcb","mcp_get_code":{"code_sha256":"38e77ca35f1f8bcb"}},{"arxiv_id":"2307.07988","paper":"/paper/motif-learning-motion-trajectories-with-local","title":"MoTIF: Learning Motion Trajectories with Local Implicit Neural Functions for Continuous Space-Time Video Super-Resolution","date":"2023-07-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"sichun233746/MoTIF","path":"option.py","file_url":"https://github.com/sichun233746/MoTIF/blob/HEAD/option.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2304.01994","paper":"/paper/dwa-differential-wavelet-amplifier-for-image","title":"Waving Goodbye to Low-Res: A Diffusion-Wavelet Approach for Image Super-Resolution","date":"2023-04-04","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"brian-moser/diwa","path":"core/logger.py","file_url":"https://github.com/brian-moser/diwa/blob/HEAD/core/logger.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2208.11284","paper":"/paper/at-ddpm-restoring-faces-degraded-by","title":"AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models","date":"2022-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"nithin-gk/at-ddpm","path":"core/logger.py","file_url":"https://github.com/nithin-gk/at-ddpm/blob/HEAD/core/logger.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2206.11892","paper":"/paper/remote-sensing-change-detection-segmentation","title":"DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection","date":"2022-06-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wgcban/ddpm-cd","path":"core/logger.py","file_url":"https://github.com/wgcban/ddpm-cd/blob/HEAD/core/logger.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2006.14200","paper":"/paper/srflow-learning-the-super-resolution-space","title":"SRFlow: Learning the Super-Resolution Space with Normalizing Flow","date":"2020-06-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liyuantsao/BFSR","path":"SRFlow-LP/code/options/options.py","file_url":"https://github.com/liyuantsao/BFSR/blob/HEAD/SRFlow-LP/code/options/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"2005.05650","paper":"/paper/invertible-image-rescaling","title":"Invertible Image Rescaling","date":"2020-05-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"JoegameZhou/ms-irn","path":"src/options/options.py","file_url":"https://github.com/JoegameZhou/ms-irn/blob/HEAD/src/options/options.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":"f729c1e42588847e","mcp_get_code":{"code_sha256":"f729c1e42588847e"}},{"arxiv_id":"1907.03222","paper":"/paper/irnet-a-general-purpose-deep-residual","title":"IRNet: A General Purpose Deep Residual Regression Framework for Materials Discovery","date":"2019-07-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/irn","path":"src/options/options.py","file_url":"https://github.com/lyqcom/irn/blob/HEAD/src/options/options.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":"f729c1e42588847e","mcp_get_code":{"code_sha256":"f729c1e42588847e"}},{"arxiv_id":"1905.02716","paper":"/paper/edvr-video-restoration-with-enhanced","title":"EDVR: Video Restoration with Enhanced Deformable Convolutional Networks","date":"2019-05-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhusiling/EDVR","path":"codes/options/options.py","file_url":"https://github.com/zhusiling/EDVR/blob/HEAD/codes/options/options.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"1609.04802","paper":"/paper/photo-realistic-single-image-super-resolution","title":"Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network","date":"2016-09-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shirsenduhalder/SRResGAN-Improved-Perceptual","path":"options/options.py","file_url":"https://github.com/shirsenduhalder/SRResGAN-Improved-Perceptual/blob/HEAD/options/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"ijcai2022_0196","paper":null,"title":"arXiv:ijcai2022_0196","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"wanghu178/KUNet","path":"codes/options/options.py","file_url":"https://github.com/wanghu178/KUNet/blob/HEAD/codes/options/options.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":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}},{"arxiv_id":"aaai_28199","paper":null,"title":"arXiv:aaai_28199","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"YoferChen/FedST","path":"core/praser.py","file_url":"https://github.com/YoferChen/FedST/blob/HEAD/core/praser.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":"38e77ca35f1f8bcb","mcp_get_code":{"code_sha256":"38e77ca35f1f8bcb"}},{"arxiv_id":"Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper","paper":null,"title":"arXiv:Xie_Diffusion-based_Event_Generation_for_High-Quality_Image_Deblurring_CVPR_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"XinanXie/EGDeblurring","path":"core/logger.py","file_url":"https://github.com/XinanXie/EGDeblurring/blob/HEAD/core/logger.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"ada7273eee7081c7","mcp_get_code":{"code_sha256":"ada7273eee7081c7"}}]}