{"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/define-d","entry":"define_D","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":17,"n_papers_ran":2,"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":17,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":18,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":1,"unverified":15},"syntology":{"atlas_url":null,"mcp":null,"mcp_per_sample":{"tool":"get_code","arguments_in":"samples[].mcp_get_code"},"developers":"https://syntology.ai/developers"},"samples":[{"arxiv_id":"2608.06184","paper":"/paper/arxiv-2608-06184","title":"EvReflection: Event-Driven Micro-Dynamics for Reflection Removal","date":null,"month_inferred_from_arxiv_id":"2026-08","title_source":"syntology","repo":"JiaxiaoWang/EvReflection","path":"models/networks.py","file_url":"https://github.com/JiaxiaoWang/EvReflection/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"10a788fe5ff7f920","mcp_get_code":{"code_sha256":"10a788fe5ff7f920"}},{"arxiv_id":"2403.14186","paper":"/paper/stylecinegan-landscape-cinemagraph-generation","title":"StyleCineGAN: Landscape Cinemagraph Generation using a Pre-trained StyleGAN","date":"2024-03-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jeolpyeoni/StyleCineGAN","path":"models/img2flow/networks.py","file_url":"https://github.com/jeolpyeoni/StyleCineGAN/blob/HEAD/models/img2flow/networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5e229d7754670457","mcp_get_code":{"code_sha256":"5e229d7754670457"}},{"arxiv_id":"2308.12538","paper":"/paper/mutual-guided-dynamic-network-for-image","title":"Mutual-Guided Dynamic Network for Image Fusion","date":"2023-08-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"guanys-dar/mgdn","path":"models/select_network.py","file_url":"https://github.com/guanys-dar/mgdn/blob/HEAD/models/select_network.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"46f02175c95e06d1","mcp_get_code":{"code_sha256":"46f02175c95e06d1"}},{"arxiv_id":"2308.10027","paper":"/paper/single-image-reflection-separation-via","title":"Single Image Reflection Separation via Component Synergy","date":"2023-08-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingcv/dsrnet","path":"models/networks.py","file_url":"https://github.com/mingcv/dsrnet/blob/HEAD/models/networks.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":"3c2906d11efdb30d","mcp_get_code":{"code_sha256":"3c2906d11efdb30d"}},{"arxiv_id":"2207.02774","paper":"/paper/local-relighting-of-real-scenes","title":"Local Relighting of Real Scenes","date":"2022-07-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"audreycui/relight","path":"models/networks.py","file_url":"https://github.com/audreycui/relight/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b05ff6ac40aab1f","mcp_get_code":{"code_sha256":"6b05ff6ac40aab1f"}},{"arxiv_id":"2110.10546","paper":"/paper/trash-or-treasure-an-interactive-dual-stream","title":"Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation","date":"2021-10-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mingcv/ytmt-strategy","path":"models/networks.py","file_url":"https://github.com/mingcv/ytmt-strategy/blob/HEAD/models/networks.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":"3c2906d11efdb30d","mcp_get_code":{"code_sha256":"3c2906d11efdb30d"}},{"arxiv_id":"2108.09752","paper":"/paper/graph2pix-a-graph-based-image-to-image","title":"Graph2Pix: A Graph-Based Image to Image Translation Framework","date":"2021-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"catlab-team/graph2pix","path":"models/networks.py","file_url":"https://github.com/catlab-team/graph2pix/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4c3ac760c14bce27","mcp_get_code":{"code_sha256":"4c3ac760c14bce27"}},{"arxiv_id":"2007.15646","paper":"/paper/rewriting-a-deep-generative-model","title":"Rewriting a Deep Generative Model","date":"2020-07-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"PeterWang512/GANSketching","path":"training/networks/misc.py","file_url":"https://github.com/PeterWang512/GANSketching/blob/HEAD/training/networks/misc.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"d976b9fd5ba3e472","mcp_get_code":{"code_sha256":"d976b9fd5ba3e472"}},{"arxiv_id":"2005.10954","paper":"/paper/head2head-video-based-neural-head-synthesis","title":"Head2Head: Video-based Neural Head Synthesis","date":"2020-05-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"michaildoukas/head2head","path":"models/networks.py","file_url":"https://github.com/michaildoukas/head2head/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"953a15dff017fbf8","mcp_get_code":{"code_sha256":"953a15dff017fbf8"}},{"arxiv_id":"1904.00637","paper":"/paper/single-image-reflection-removal-exploiting","title":"Single Image Reflection Removal Exploiting Misaligned Training Data and Network Enhancements","date":"2019-04-01","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Vandermode/ERRNet","path":"models/networks.py","file_url":"https://github.com/Vandermode/ERRNet/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2c137d3584064330","mcp_get_code":{"code_sha256":"2c137d3584064330"}},{"arxiv_id":"1811.12641","paper":"/paper/transferable-adversarial-attacks-for-image","title":"Transferable Adversarial Attacks for Image and Video Object Detection","date":"2018-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LiangSiyuan21/Adversarial-Attacks-for-Image-and-Video-Object-Detection","path":"img_attack_with_attention/model/GAN.py","file_url":"https://github.com/LiangSiyuan21/Adversarial-Attacks-for-Image-and-Video-Object-Detection/blob/HEAD/img_attack_with_attention/model/GAN.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"66bf9e7084c27743","mcp_get_code":{"code_sha256":"66bf9e7084c27743"}},{"arxiv_id":"1805.04487","paper":"/paper/non-stationary-texture-synthesis-by","title":"Non-Stationary Texture Synthesis by Adversarial Expansion","date":"2018-05-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"jessemelpolio/non-stationary_texture_syn","path":"models/networks.py","file_url":"https://github.com/jessemelpolio/non-stationary_texture_syn/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"659e3395d9f8e555","mcp_get_code":{"code_sha256":"659e3395d9f8e555"}},{"arxiv_id":"1804.08864","paper":"/paper/learning-to-see-the-invisible-end-to-end","title":"Learning to See the Invisible: End-to-End Trainable Amodal Instance Segmentation","date":"2018-04-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apchenstu/SLN-Amodal","path":"modal/networks.py","file_url":"https://github.com/apchenstu/SLN-Amodal/blob/HEAD/modal/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"250745e5289f8fe5","mcp_get_code":{"code_sha256":"250745e5289f8fe5"}},{"arxiv_id":"1711.11585","paper":"/paper/high-resolution-image-synthesis-and-semantic","title":"High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs","date":"2017-11-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ayanglab/cs2","path":"network.py","file_url":"https://github.com/ayanglab/cs2/blob/HEAD/network.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":"1f234fca8639da36","mcp_get_code":{"code_sha256":"1f234fca8639da36"}},{"arxiv_id":"1703.10593","paper":"/paper/unpaired-image-to-image-translation-using","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","date":"2017-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ranery/Bayesian-CycleGAN","path":"models/networks.py","file_url":"https://github.com/ranery/Bayesian-CycleGAN/blob/HEAD/models/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"514b952ec6180601","mcp_get_code":{"code_sha256":"514b952ec6180601"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"telecombcn-dl/2018-dlcv-team2","path":"network_transfer.py","file_url":"https://github.com/telecombcn-dl/2018-dlcv-team2/blob/HEAD/network_transfer.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"c3c1c6a5bed28673","mcp_get_code":{"code_sha256":"c3c1c6a5bed28673"}},{"arxiv_id":"1611.07004","paper":"/paper/image-to-image-translation-with-conditional","title":"Image-to-Image Translation with Conditional Adversarial Networks","date":"2016-11-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"telecombcn-dl/2018-dlcv-team2","path":"networks.py","file_url":"https://github.com/telecombcn-dl/2018-dlcv-team2/blob/HEAD/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6152413276c330a5","mcp_get_code":{"code_sha256":"6152413276c330a5"}},{"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":"models/fedst_ddpm_model/networks.py","file_url":"https://github.com/YoferChen/FedST/blob/HEAD/models/fedst_ddpm_model/networks.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":"f96be7289f55512e","mcp_get_code":{"code_sha256":"f96be7289f55512e"}}]}