{"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/random-flip","entry":"random_flip","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":12,"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":8,"n_samples_ran":1,"n_samples_fingerprinted":1,"n_places":12,"n_places_pointer_only":2,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":1,"ran":0,"unverified":7},"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":"2210.06702","paper":"/paper/a-mixture-of-surprises-for-unsupervised","title":"A Mixture of Surprises for Unsupervised Reinforcement Learning","date":"2022-10-13","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeapLabTHU/MOSS","path":"core/calculations/augmentations.py","file_url":"https://github.com/LeapLabTHU/MOSS/blob/HEAD/core/calculations/augmentations.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":"694ec6f6ee783784","mcp_get_code":{"code_sha256":"694ec6f6ee783784"}},{"arxiv_id":"2108.06552","paper":"/paper/weakly-supervised-continual-learning","title":"Continual Semi-Supervised Learning through Contrastive Interpolation Consistency","date":"2021-08-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"loribonna/cssl","path":"models/ccic.py","file_url":"https://github.com/loribonna/cssl/blob/HEAD/models/ccic.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"9c7d99ad8fa869d9","mcp_get_code":{"code_sha256":"9c7d99ad8fa869d9"}},{"arxiv_id":"2106.12699","paper":"/paper/distilling-the-knowledge-from-normalizing","title":"Distilling the Knowledge from Conditional Normalizing Flows","date":"2021-06-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"yandex-research/distill-nf","path":"srflow/datasets.py","file_url":"https://github.com/yandex-research/distill-nf/blob/HEAD/srflow/datasets.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":"1ce86501baae76b7","mcp_get_code":{"code_sha256":"1ce86501baae76b7"}},{"arxiv_id":"2008.06775","paper":"/paper/model-patching-closing-the-subgroup","title":"Model Patching: Closing the Subgroup Performance Gap with Data Augmentation","date":"2020-08-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"HazyResearch/model-patching","path":"augmentation/autoaugment/augmentation_transforms.py","file_url":"https://github.com/HazyResearch/model-patching/blob/HEAD/augmentation/autoaugment/augmentation_transforms.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":"6e1198a9c8c47438","mcp_get_code":{"code_sha256":"6e1198a9c8c47438"}},{"arxiv_id":"2006.04730","paper":"/paper/picket-self-supervised-data-diagnostics-for","title":"Picket: Guarding Against Corrupted Data in Tabular Data during Learning and Inference","date":"2020-06-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"rekords-uw/Picket","path":"picket/prepare/dataPrepare.py","file_url":"https://github.com/rekords-uw/Picket/blob/HEAD/picket/prepare/dataPrepare.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"b0f137dc86a5d3f1","mcp_get_code":{"code_sha256":"b0f137dc86a5d3f1"}},{"arxiv_id":"2004.14990","paper":"/paper/reinforcement-learning-with-augmented-data","title":"Reinforcement Learning with Augmented Data","date":"2020-04-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"MishaLaskin/rad","path":"curl_sac.py","file_url":"https://github.com/MishaLaskin/rad/blob/HEAD/curl_sac.py","status":"ran_fixture","verification_level":1,"contract_check":"RAISES","metamorphic_tier":null,"behaviour_fingerprint":true,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"0c0e4ec30cabf972","mcp_get_code":{"code_sha256":"0c0e4ec30cabf972"}},{"arxiv_id":"2003.06792","paper":"/paper/learning-enriched-features-for-real-image","title":"Learning Enriched Features for Real Image Restoration and Enhancement","date":"2020-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"venkat2319/Mirnet","path":"mirnet/dataloader/common.py","file_url":"https://github.com/venkat2319/Mirnet/blob/HEAD/mirnet/dataloader/common.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":"2a3989cdfa8362a3","mcp_get_code":{"code_sha256":"2a3989cdfa8362a3"}},{"arxiv_id":"1905.05393","paper":"/paper/190505393","title":"Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules","date":"2019-05-14","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"arcelien/pba","path":"autoaugment/augmentation_transforms.py","file_url":"https://github.com/arcelien/pba/blob/HEAD/autoaugment/augmentation_transforms.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":"6e1198a9c8c47438","mcp_get_code":{"code_sha256":"6e1198a9c8c47438"}},{"arxiv_id":"1904.12848","paper":"/paper/unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"google-research/uda","path":"image/randaugment/augmentation_transforms.py","file_url":"https://github.com/google-research/uda/blob/HEAD/image/randaugment/augmentation_transforms.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":"6e1198a9c8c47438","mcp_get_code":{"code_sha256":"6e1198a9c8c47438"}},{"arxiv_id":"1808.08718","paper":"/paper/wide-activation-for-efficient-and-accurate","title":"Wide Activation for Efficient and Accurate Image Super-Resolution","date":"2018-08-27","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krasserm/super-resolution","path":"data.py","file_url":"https://github.com/krasserm/super-resolution/blob/HEAD/data.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":"256bed53fab6520f","mcp_get_code":{"code_sha256":"256bed53fab6520f"}},{"arxiv_id":"1805.09501","paper":"/paper/autoaugment-learning-augmentation-policies","title":"AutoAugment: Learning Augmentation Policies from Data","date":"2018-05-24","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"YaCpotato/deepaugmentFix","path":"deepaugment/image_generator.py","file_url":"https://github.com/YaCpotato/deepaugmentFix/blob/HEAD/deepaugment/image_generator.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"6e1198a9c8c47438","mcp_get_code":{"code_sha256":"6e1198a9c8c47438"}},{"arxiv_id":"1602.07868","paper":"/paper/weight-normalization-a-simple","title":"Weight Normalization: A Simple Reparameterization to Accelerate Training of Deep Neural Networks","date":"2016-02-25","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"krasserm/wdsr","path":"data.py","file_url":"https://github.com/krasserm/wdsr/blob/HEAD/data.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":"256bed53fab6520f","mcp_get_code":{"code_sha256":"256bed53fab6520f"}}]}