{"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/get-discriminator","entry":"get_discriminator","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":7,"n_papers_ran":3,"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":7,"n_samples_ran":3,"n_samples_fingerprinted":0,"n_places":7,"n_places_pointer_only":3,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":1,"ran_fixture":0,"ran":2,"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":"2403.01189","paper":"/paper/training-unbiased-diffusion-models-from","title":"Training Unbiased Diffusion Models From Biased Dataset","date":"2024-03-02","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"alsdudrla10/TIW-DSM","path":"classifier_lib.py","file_url":"https://github.com/alsdudrla10/TIW-DSM/blob/HEAD/classifier_lib.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NOASSERTION","inline_ok":false,"code_sha256_prefix":"624251787df4cb51","mcp_get_code":{"code_sha256":"624251787df4cb51"}},{"arxiv_id":"2010.06897","paper":"/paper/adaptive-attentive-geolocalization-from-few","title":"Adaptive-Attentive Geolocalization from few queries: a hybrid approach","date":"2020-10-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"valeriopaolicelli/AdAGeo","path":"src/grl_util.py","file_url":"https://github.com/valeriopaolicelli/AdAGeo/blob/HEAD/src/grl_util.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":"8863d45315243613","mcp_get_code":{"code_sha256":"8863d45315243613"}},{"arxiv_id":"2003.10477","paper":"/paper/distillating-knowledge-from-graph","title":"Distilling Knowledge from Graph Convolutional Networks","date":"2020-03-23","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"ihollywhy/DistillGCN.PyTorch","path":"discriminator_model.py","file_url":"https://github.com/ihollywhy/DistillGCN.PyTorch/blob/HEAD/discriminator_model.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"f529fb33becac8f2","mcp_get_code":{"code_sha256":"f529fb33becac8f2"}},{"arxiv_id":"1902.06965","paper":"/paper/dedpul-method-for-mixture-proportion","title":"DEDPUL: Difference-of-Estimated-Densities-based Positive-Unlabeled Learning","date":"2019-02-19","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"dimonenka/DEDPUL","path":"NN_functions.py","file_url":"https://github.com/dimonenka/DEDPUL/blob/HEAD/NN_functions.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"fdf1ad5d8442958e","mcp_get_code":{"code_sha256":"fdf1ad5d8442958e"}},{"arxiv_id":"1703.10593","paper":"/paper/unpaired-image-to-image-translation-using","title":"Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks","date":"2017-03-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yex666/cyclegan-in-mindspore","path":"src/models/cycle_gan.py","file_url":"https://github.com/yex666/cyclegan-in-mindspore/blob/HEAD/src/models/cycle_gan.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":"f8a056c53e46014b","mcp_get_code":{"code_sha256":"f8a056c53e46014b"}},{"arxiv_id":"1409.7495","paper":"/paper/unsupervised-domain-adaptation-by","title":"Unsupervised Domain Adaptation by Backpropagation","date":"2014-09-26","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"chenkang121/DANN","path":"DANN_minit_to_mnist_m.py","file_url":"https://github.com/chenkang121/DANN/blob/HEAD/DANN_minit_to_mnist_m.py","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":"none","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"2a2818b6a3f495d4","mcp_get_code":{"code_sha256":"2a2818b6a3f495d4"}},{"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":"FrankwaP/pytorch-toy-GAN","path":"networks.py","file_url":"https://github.com/FrankwaP/pytorch-toy-GAN/blob/HEAD/networks.py","status":"ran_draft_wrong","verification_level":1,"contract_check":"OUTPUT_MISDECLARED","metamorphic_tier":"deterministic","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"898b29fd92fffb72","mcp_get_code":{"code_sha256":"898b29fd92fffb72"}}]}