{"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/spectralnorm","entry":"SpectralNorm","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":9,"n_papers_ran":8,"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":15,"n_samples_ran":12,"n_samples_fingerprinted":1,"n_places":15,"n_places_pointer_only":12,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":11,"unverified":3},"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":"2408.12316","paper":"/paper/unrolled-decomposed-unpaired-learning-for","title":"Unrolled Decomposed Unpaired Learning for Controllable Low-Light Video Enhancement","date":"2024-08-22","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lingyzhu0101/UDU","path":"src/models.py","file_url":"https://github.com/lingyzhu0101/UDU/blob/HEAD/src/models.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"28f7a5c00218727e","mcp_get_code":{"code_sha256":"28f7a5c00218727e"}},{"arxiv_id":"2403.10543","paper":"/paper/distinguishing-neighborhood-representations","title":"Mitigating Oversmoothing Through Reverse Process of GNNs for Heterophilic Graphs","date":"2024-03-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ml-postech/reverse-gnn","path":"src/model_resgnn_rep.py","file_url":"https://github.com/ml-postech/reverse-gnn/blob/HEAD/src/model_resgnn_rep.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3ad606c7b21a06b0","mcp_get_code":{"code_sha256":"3ad606c7b21a06b0"}},{"arxiv_id":"2311.02202","paper":"/paper/neural-collage-transfer-artistic-1","title":"Neural Collage Transfer: Artistic Reconstruction via Material Manipulation","date":"2023-11-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"northadventure/CollageRL","path":"env/collage.py","file_url":"https://github.com/northadventure/CollageRL/blob/HEAD/env/collage.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"fb56e8949300284c","mcp_get_code":{"code_sha256":"fb56e8949300284c"}},{"arxiv_id":"2203.15662","paper":"/paper/matteformer-transformer-based-image-matting","title":"MatteFormer: Transformer-Based Image Matting via Prior-Tokens","date":"2022-03-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"webtoon/matteformer","path":"networks/encoders/MatteFormer.py","file_url":"https://github.com/webtoon/matteformer/blob/HEAD/networks/encoders/MatteFormer.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":"e489b0e620c2827e","mcp_get_code":{"code_sha256":"e489b0e620c2827e"}},{"arxiv_id":"2104.05170","paper":"/paper/memory-guided-unsupervised-image-to-image","title":"Memory-guided Unsupervised Image-to-image Translation","date":"2021-04-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HsinYingLee/DRIT","path":"src/model.py","file_url":"https://github.com/HsinYingLee/DRIT/blob/HEAD/src/model.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"43fb0d94c6f43e76","mcp_get_code":{"code_sha256":"43fb0d94c6f43e76"}},{"arxiv_id":"2006.08265","paper":"/paper/gs-wgan-a-gradient-sanitized-approach-for","title":"GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators","date":"2020-06-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DingfanChen/GS-WGAN","path":"source/models.py","file_url":"https://github.com/DingfanChen/GS-WGAN/blob/HEAD/source/models.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"ce077451cdef92e9","mcp_get_code":{"code_sha256":"ce077451cdef92e9"}},{"arxiv_id":"2002.04114","paper":"/paper/cross-modality-paired-images-generation-for","title":"Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification","date":"2020-02-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wangguanan/JSIA-ReID","path":"core/networks.py","file_url":"https://github.com/wangguanan/JSIA-ReID/blob/HEAD/core/networks.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"a19c1e7fabfd2c4b","mcp_get_code":{"code_sha256":"a19c1e7fabfd2c4b"}},{"arxiv_id":"2001.02332","paper":"/paper/generative-adversarial-zero-shot-relational","title":"Generative Adversarial Zero-Shot Relational Learning for Knowledge Graphs","date":"2020-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Panda0406/Zero-shot-knowledge-graph-relational-learning","path":"Networks.py","file_url":"https://github.com/Panda0406/Zero-shot-knowledge-graph-relational-learning/blob/HEAD/Networks.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"9567f5ebe9641be4","mcp_get_code":{"code_sha256":"9567f5ebe9641be4"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"karolrogozinski/cern_alice_fast_sim_corrvae","path":"utils/spectral_norm_fc.py","file_url":"https://github.com/karolrogozinski/cern_alice_fast_sim_corrvae/blob/HEAD/utils/spectral_norm_fc.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eee46d1790d3fc3d","mcp_get_code":{"code_sha256":"eee46d1790d3fc3d"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"meg965/pytorch-rl","path":"Layers/Spectral_norm.py","file_url":"https://github.com/meg965/pytorch-rl/blob/HEAD/Layers/Spectral_norm.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"c11fdae8c1eaa067","mcp_get_code":{"code_sha256":"c11fdae8c1eaa067"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"koshian2/SNGAN","path":"models/core_layers.py","file_url":"https://github.com/koshian2/SNGAN/blob/HEAD/models/core_layers.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a587e964e3b73e7f","mcp_get_code":{"code_sha256":"a587e964e3b73e7f"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hanyoseob/pytorch-sngan","path":"layer.py","file_url":"https://github.com/hanyoseob/pytorch-sngan/blob/HEAD/layer.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"95f6381171e5b22f","mcp_get_code":{"code_sha256":"95f6381171e5b22f"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"apnkv/nla_spectral_norm","path":"spec_norm_diff.py","file_url":"https://github.com/apnkv/nla_spectral_norm/blob/HEAD/spec_norm_diff.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"eea0b4fc0256e64c","mcp_get_code":{"code_sha256":"eea0b4fc0256e64c"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"hinofafa/Self-Attention-HearthStone-GAN","path":"spectral.py","file_url":"https://github.com/hinofafa/Self-Attention-HearthStone-GAN/blob/HEAD/spectral.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5c6f3d4b49c16aa5","mcp_get_code":{"code_sha256":"5c6f3d4b49c16aa5"}},{"arxiv_id":"1802.05957","paper":"/paper/spectral-normalization-for-generative","title":"Spectral Normalization for Generative Adversarial Networks","date":"2018-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"bayraktarbaris/SNGAN","path":"spectral_normalization.py","file_url":"https://github.com/bayraktarbaris/SNGAN/blob/HEAD/spectral_normalization.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"cb5d0b5f94066d27","mcp_get_code":{"code_sha256":"cb5d0b5f94066d27"}}]}