{"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/numpy-calculate-frechet-distance","entry":"numpy_calculate_frechet_distance","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":8,"n_papers_ran":0,"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":4,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":8,"n_places_pointer_only":0,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":4},"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":"2206.09479","paper":"/paper/studiogan-a-taxonomy-and-benchmark-of-gans","title":"StudioGAN: A Taxonomy and Benchmark of GANs for Image Synthesis","date":"2022-06-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"lyqcom/biggan","path":"src/inception_utils.py","file_url":"https://github.com/lyqcom/biggan/blob/HEAD/src/inception_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":"69b2abc7f7010394","mcp_get_code":{"code_sha256":"69b2abc7f7010394"}},{"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":"inception_utils.py","file_url":"https://github.com/ajbrock/BigGAN-PyTorch/blob/HEAD/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54af0200eab3163f","mcp_get_code":{"code_sha256":"54af0200eab3163f"}},{"arxiv_id":"2004.00917","paper":"/paper/controllable-orthogonalization-in-training","title":"Controllable Orthogonalization in Training DNNs","date":"2020-04-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"huangleiBuaa/ONI","path":"ONI_PyTorch/GAN/inception_utils.py","file_url":"https://github.com/huangleiBuaa/ONI/blob/HEAD/ONI_PyTorch/GAN/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"BSD-2-Clause","inline_ok":true,"code_sha256_prefix":"54af0200eab3163f","mcp_get_code":{"code_sha256":"54af0200eab3163f"}},{"arxiv_id":"1912.05270","paper":"/paper/minegan-effective-knowledge-transfer-from","title":"MineGAN: effective knowledge transfer from GANs to target domains with few images","date":"2019-12-11","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yaxingwang/MineGAN","path":"inception_utils.py","file_url":"https://github.com/yaxingwang/MineGAN/blob/HEAD/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54af0200eab3163f","mcp_get_code":{"code_sha256":"54af0200eab3163f"}},{"arxiv_id":"1811.10597","paper":"/paper/gan-dissection-visualizing-and-understanding","title":"GAN Dissection: Visualizing and Understanding Generative Adversarial Networks","date":"2018-11-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alexandonian/ganocracy","path":"gan_training/ganocracy/metrics/inception_score.py","file_url":"https://github.com/alexandonian/ganocracy/blob/HEAD/gan_training/ganocracy/metrics/inception_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"7f2187cc95ef4c91","mcp_get_code":{"code_sha256":"7f2187cc95ef4c91"}},{"arxiv_id":"1406.2661","paper":"/paper/generative-adversarial-networks","title":"Generative Adversarial Networks","date":"2014-06-10","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"WANG-Chaoyue/EvolutionaryGAN-pytorch","path":"inception_pytorch/inception_utils.py","file_url":"https://github.com/WANG-Chaoyue/EvolutionaryGAN-pytorch/blob/HEAD/inception_pytorch/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54af0200eab3163f","mcp_get_code":{"code_sha256":"54af0200eab3163f"}},{"arxiv_id":"aaai_20902","paper":null,"title":"arXiv:aaai_20902","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"bxz9200/CLPA","path":"inception_utils.py","file_url":"https://github.com/bxz9200/CLPA/blob/HEAD/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"54af0200eab3163f","mcp_get_code":{"code_sha256":"54af0200eab3163f"}},{"arxiv_id":"aaai_20247","paper":null,"title":"arXiv:aaai_20247","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"csmiler/ProbeGAN","path":"inception_utils.py","file_url":"https://github.com/csmiler/ProbeGAN/blob/HEAD/inception_utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"5c232e0c21db4894","mcp_get_code":{"code_sha256":"5c232e0c21db4894"}}]}