{"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/denorm","entry":"denorm","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":23,"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":14,"n_samples_ran":4,"n_samples_fingerprinted":4,"n_places":24,"n_places_pointer_only":2,"by_status":{"ran_honours":1,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"unverified":10},"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":"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":"sample_adv_batch.py","file_url":"https://github.com/ryliu68/CDMA/blob/HEAD/sample_adv_batch.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"4752f3a132bde7b3","mcp_get_code":{"code_sha256":"4752f3a132bde7b3"}},{"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":"utils.py","file_url":"https://github.com/abstractteen/gan_ownership_verification/blob/HEAD/utils.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"8466931f776cda0a","mcp_get_code":{"code_sha256":"8466931f776cda0a"}},{"arxiv_id":"2305.01644","paper":"/paper/key-locked-rank-one-editing-for-text-to-image","title":"Key-Locked Rank One Editing for Text-to-Image Personalization","date":"2023-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization","path":"clipseg/evaluation_utils.py","file_url":"https://github.com/ChenDarYen/Key-Locked-Rank-One-Editing-for-Text-to-Image-Personalization/blob/HEAD/clipseg/evaluation_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"17efe0b1bfd9f343","mcp_get_code":{"code_sha256":"17efe0b1bfd9f343"}},{"arxiv_id":"2212.10305","paper":"/paper/which-pixel-to-annotate-a-label-efficient","title":"Which Pixel to Annotate: a Label-Efficient Nuclei Segmentation Framework","date":"2022-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lhaof/nuseg","path":"CSinGAN/SinGAN/imresize.py","file_url":"https://github.com/lhaof/nuseg/blob/HEAD/CSinGAN/SinGAN/imresize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"2211.12445","paper":"/paper/sindiffusion-learning-a-diffusion-model-from","title":"SinDiffusion: Learning a Diffusion Model from a Single Natural Image","date":"2022-11-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"weilunwang/sindiffusion","path":"guided_diffusion/imresize.py","file_url":"https://github.com/weilunwang/sindiffusion/blob/HEAD/guided_diffusion/imresize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"2208.07591","paper":"/paper/uncertainty-guided-source-free-domain","title":"Uncertainty-guided Source-free Domain Adaptation","date":"2022-08-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"roysubhankar/uncertainty-sfda","path":"cifar/data_list.py","file_url":"https://github.com/roysubhankar/uncertainty-sfda/blob/HEAD/cifar/data_list.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e16337c88174e00","mcp_get_code":{"code_sha256":"6e16337c88174e00"}},{"arxiv_id":"2201.12179","paper":"/paper/plug-play-attacks-towards-robust-and-flexible","title":"Plug & Play Attacks: Towards Robust and Flexible Model Inversion Attacks","date":"2022-01-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ajbrock/BigGAN-PyTorch","path":"TFHub/biggan_v1.py","file_url":"https://github.com/ajbrock/BigGAN-PyTorch/blob/HEAD/TFHub/biggan_v1.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e16337c88174e00","mcp_get_code":{"code_sha256":"6e16337c88174e00"}},{"arxiv_id":"2111.02363","paper":"/paper/deep-learning-based-non-intrusive-multi","title":"Deep Learning-based Non-Intrusive Multi-Objective Speech Assessment Model with Cross-Domain Features","date":"2021-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dhimasryan/MOSA-Net-Cross-Domain","path":"MOSA_Net+/MOSA_Net_plus.py","file_url":"https://github.com/dhimasryan/MOSA-Net-Cross-Domain/blob/HEAD/MOSA_Net%2B/MOSA_Net_plus.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"1f689cfa346f0880","mcp_get_code":{"code_sha256":"1f689cfa346f0880"}},{"arxiv_id":"2110.02900","paper":"/paper/meta-internal-learning","title":"Meta Internal Learning","date":"2021-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"RaphaelBensTAU/MetaInternalLearning","path":"modules/imresize.py","file_url":"https://github.com/RaphaelBensTAU/MetaInternalLearning/blob/HEAD/modules/imresize.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"2109.15222","paper":"/paper/self-supervised-out-of-distribution-detection-1","title":"Natural Synthetic Anomalies for Self-Supervised Anomaly Detection and Localization","date":"2021-09-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hmsch/natural-synthetic-anomalies","path":"experiments/plotting_utils.py","file_url":"https://github.com/hmsch/natural-synthetic-anomalies/blob/HEAD/experiments/plotting_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"920324b9e7cc6816","mcp_get_code":{"code_sha256":"920324b9e7cc6816"}},{"arxiv_id":"2105.11120","paper":"/paper/a-fourier-based-framework-for-domain","title":"A Fourier-based Framework for Domain Generalization","date":"2021-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MediaBrain-SJTU/FACT","path":"utils/tools.py","file_url":"https://github.com/MediaBrain-SJTU/FACT/blob/HEAD/utils/tools.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f2d413920bb77b76","mcp_get_code":{"code_sha256":"f2d413920bb77b76"}},{"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":"sangyun884/fast-ode","path":"guided_diffusion/gaussian_diffusion.py","file_url":"https://github.com/sangyun884/fast-ode/blob/HEAD/guided_diffusion/gaussian_diffusion.py","status":"ran_honours","verification_level":1,"contract_check":"HONOURS","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"f1c35d187bd5b161","mcp_get_code":{"code_sha256":"f1c35d187bd5b161"}},{"arxiv_id":"2010.02637","paper":"/paper/disentangled-generative-causal-representation-1","title":"Weakly Supervised Disentangled Generative Causal Representation Learning","date":"2020-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xwshen51/DEAR","path":"utils.py","file_url":"https://github.com/xwshen51/DEAR/blob/HEAD/utils.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":"6e16337c88174e00","mcp_get_code":{"code_sha256":"6e16337c88174e00"}},{"arxiv_id":"2008.01065","paper":"/paper/memory-augmented-dense-predictive-coding-for","title":"Memory-augmented Dense Predictive Coding for Video Representation Learning","date":"2020-08-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"TengdaHan/MemDPC","path":"utils/utils.py","file_url":"https://github.com/TengdaHan/MemDPC/blob/HEAD/utils/utils.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":"58313a54c09c8841","mcp_get_code":{"code_sha256":"58313a54c09c8841"}},{"arxiv_id":"2003.11512","paper":"/paper/improved-techniques-for-training-single-image","title":"Improved Techniques for Training Single-Image GANs","date":"2020-03-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"tohinz/ConSinGAN","path":"ConSinGAN/functions.py","file_url":"https://github.com/tohinz/ConSinGAN/blob/HEAD/ConSinGAN/functions.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"2001.06001","paper":"/paper/curriculum-labeling-self-paced-pseudo","title":"Curriculum Labeling: Revisiting Pseudo-Labeling for Semi-Supervised Learning","date":"2020-01-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uvavision/Curriculum-Labeling","path":"utils/dataloaders.py","file_url":"https://github.com/uvavision/Curriculum-Labeling/blob/HEAD/utils/dataloaders.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"1905.01723","paper":"/paper/few-shot-unsupervised-image-to-image","title":"Few-Shot Unsupervised Image-to-Image Translation","date":"2019-05-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"CV-Reimplementation/FUNIT-Reimplementation","path":"utils.py","file_url":"https://github.com/CV-Reimplementation/FUNIT-Reimplementation/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b755d5dfbc732b7f","mcp_get_code":{"code_sha256":"b755d5dfbc732b7f"}},{"arxiv_id":"1905.01164","paper":"/paper/singan-learning-a-generative-model-from-a","title":"SinGAN: Learning a Generative Model from a Single Natural Image","date":"2019-05-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ilyak93/SinGan","path":"SinGAN/functions.py","file_url":"https://github.com/ilyak93/SinGan/blob/HEAD/SinGAN/functions.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":true,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"289ace78904e68a8","mcp_get_code":{"code_sha256":"289ace78904e68a8"}},{"arxiv_id":"1809.11096","paper":"/paper/large-scale-gan-training-for-high-fidelity","title":"Large Scale GAN Training for High Fidelity Natural Image Synthesis","date":"2018-09-28","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kidist-amde/biggan-pytorch","path":"TFHub/biggan_v1.py","file_url":"https://github.com/kidist-amde/biggan-pytorch/blob/HEAD/TFHub/biggan_v1.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e16337c88174e00","mcp_get_code":{"code_sha256":"6e16337c88174e00"}},{"arxiv_id":"1805.08318","paper":"/paper/self-attention-generative-adversarial","title":"Self-Attention Generative Adversarial Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"voletiv/self-attention-GAN-pytorch","path":"utils.py","file_url":"https://github.com/voletiv/self-attention-GAN-pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e16337c88174e00","mcp_get_code":{"code_sha256":"6e16337c88174e00"}},{"arxiv_id":"1805.08318","paper":"/paper/self-attention-generative-adversarial","title":"Self-Attention Generative Adversarial Networks","date":"2018-05-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Atmosphere-art/Self-Attention-GAN","path":"utils.py","file_url":"https://github.com/Atmosphere-art/Self-Attention-GAN/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c7967df1b87b856","mcp_get_code":{"code_sha256":"9c7967df1b87b856"}},{"arxiv_id":"1803.02735","paper":"/paper/deep-back-projection-networks-for-super","title":"Deep Back-Projection Networks For Super-Resolution","date":"2018-03-07","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alterzero/DBPN-Pytorch","path":"utils.py","file_url":"https://github.com/alterzero/DBPN-Pytorch/blob/HEAD/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"754eaaec87e45177","mcp_get_code":{"code_sha256":"754eaaec87e45177"}},{"arxiv_id":"aaai_25456","paper":null,"title":"arXiv:aaai_25456","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SuhZhang/ConvMatch","path":"core/valid.py","file_url":"https://github.com/SuhZhang/ConvMatch/blob/HEAD/core/valid.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b5ee9fe27e960f1","mcp_get_code":{"code_sha256":"6b5ee9fe27e960f1"}},{"arxiv_id":"Zhang_DeMatch_Deep_Decomposition_of_Motion_Field_for_Two-View_Correspondence_Learning_CVPR_2024_paper","paper":null,"title":"arXiv:Zhang_DeMatch_Deep_Decomposition_of_Motion_Field_for_Two-View_Correspondence_Learning_CVPR_2024_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"SuhZhang/DeMatch","path":"core/valid.py","file_url":"https://github.com/SuhZhang/DeMatch/blob/HEAD/core/valid.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6b5ee9fe27e960f1","mcp_get_code":{"code_sha256":"6b5ee9fe27e960f1"}}]}