{"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/calculate-inception-score","entry":"calculate_inception_score","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":5,"n_papers_ran":1,"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":5,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":5,"n_places_pointer_only":1,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"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":"2412.04653","paper":"/paper/hidden-in-the-noise-two-stage-robust","title":"Hidden in the Noise: Two-Stage Robust Watermarking for Images","date":"2024-12-05","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"Kasraarabi/Hidden-in-the-Noise","path":"inpainting.py","file_url":"https://github.com/Kasraarabi/Hidden-in-the-Noise/blob/HEAD/inpainting.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"44b81851089251c6","mcp_get_code":{"code_sha256":"44b81851089251c6"}},{"arxiv_id":"2303.04743","paper":"/paper/vector-quantized-time-series-generation-with","title":"Vector Quantized Time Series Generation with a Bidirectional Prior Model","date":"2023-03-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"danelee2601/supervised-fcn","path":"supervised_FCN/example_compute_IS.py","file_url":"https://github.com/danelee2601/supervised-fcn/blob/HEAD/supervised_FCN/example_compute_IS.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"01a41e038c0e0ec8","mcp_get_code":{"code_sha256":"01a41e038c0e0ec8"}},{"arxiv_id":"2302.01952","paper":"/paper/on-a-continuous-time-model-of-gradient","title":"On a continuous time model of gradient descent dynamics and instability in deep learning","date":"2023-02-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"deepmind/dd_two_player_games","path":"dd_two_player_games/metric_utils.py","file_url":"https://github.com/deepmind/dd_two_player_games/blob/HEAD/dd_two_player_games/metric_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":"91a2171efdef64bc","mcp_get_code":{"code_sha256":"91a2171efdef64bc"}},{"arxiv_id":"2205.15677","paper":"/paper/augmentation-aware-self-supervision-for-data-1","title":"Augmentation-Aware Self-Supervision for Data-Efficient GAN Training","date":"2022-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"liang-hou/augself-gan","path":"augself-biggan/inception_tf.py","file_url":"https://github.com/liang-hou/augself-gan/blob/HEAD/augself-biggan/inception_tf.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"16c22401d84c7ba1","mcp_get_code":{"code_sha256":"16c22401d84c7ba1"}},{"arxiv_id":"2111.06636","paper":"/paper/closed-loop-data-transcription-to-an-ldr-via-1","title":"Closed-Loop Data Transcription to an LDR via Minimaxing Rate Reduction","date":"2021-11-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"delay-xili/ldr","path":"evaluation.py","file_url":"https://github.com/delay-xili/ldr/blob/HEAD/evaluation.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":"5be9ef16afc97a75","mcp_get_code":{"code_sha256":"5be9ef16afc97a75"}}]}