{"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/compute-prd","entry":"compute_prd","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":4,"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":3,"n_samples_ran":2,"n_samples_fingerprinted":0,"n_places":5,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":2,"ran":0,"unverified":1},"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":"2010.15639","paper":"/paper/teaching-a-gan-what-not-to-learn","title":"Teaching a GAN What Not to Learn","date":"2020-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"DarthSid95/RumiGANs","path":"ext_resources/prd_score.py","file_url":"https://github.com/DarthSid95/RumiGANs/blob/HEAD/ext_resources/prd_score.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b9da61c36d3b8f11","mcp_get_code":{"code_sha256":"b9da61c36d3b8f11"}},{"arxiv_id":"1912.04958","paper":"/paper/analyzing-and-improving-the-image-quality-of","title":"Analyzing and Improving the Image Quality of StyleGAN","date":"2019-12-03","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"claim-berlin/3d_stylegan_circle_of_willis","path":"MedicalNet/evaluation.py","file_url":"https://github.com/claim-berlin/3d_stylegan_circle_of_willis/blob/HEAD/MedicalNet/evaluation.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"b83c9dfb41ea1c2a","mcp_get_code":{"code_sha256":"b83c9dfb41ea1c2a"}},{"arxiv_id":"1905.10485","paper":"/paper/generative-latent-flow-a-framework-for-non","title":"Generative Latent Flow","date":"2019-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rakhimovv/GenerativeLatentFlow","path":"glf/metrics/prd_score.py","file_url":"https://github.com/rakhimovv/GenerativeLatentFlow/blob/HEAD/glf/metrics/prd_score.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b9da61c36d3b8f11","mcp_get_code":{"code_sha256":"b9da61c36d3b8f11"}},{"arxiv_id":"1807.04720","paper":"/paper/the-gan-landscape-losses-architectures","title":"A Large-Scale Study on Regularization and Normalization in GANs","date":"2018-07-12","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"xiao7199/compare_gan","path":"compare_gan/src/prd_score.py","file_url":"https://github.com/xiao7199/compare_gan/blob/HEAD/compare_gan/src/prd_score.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":false,"code_sha256_prefix":"91b26d9e0c4aab9b","mcp_get_code":{"code_sha256":"91b26d9e0c4aab9b"}},{"arxiv_id":"1806.00035","paper":"/paper/assessing-generative-models-via-precision-and","title":"Assessing Generative Models via Precision and Recall","date":"2018-05-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"msmsajjadi/precision-recall-distributions","path":"prd_score.py","file_url":"https://github.com/msmsajjadi/precision-recall-distributions/blob/HEAD/prd_score.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"Apache-2.0","inline_ok":true,"code_sha256_prefix":"b9da61c36d3b8f11","mcp_get_code":{"code_sha256":"b9da61c36d3b8f11"}}]}