{"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-cifar10","entry":"get_cifar10","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":13,"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":14,"n_samples_ran":0,"n_samples_fingerprinted":0,"n_places":15,"n_places_pointer_only":6,"by_status":{"ran_honours":0,"ran_violates":0,"ran_draft_wrong":0,"ran_fixture":0,"ran":0,"unverified":14},"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":"2410.06109","paper":"/paper/continuous-contrastive-learning-for-long","title":"Continuous Contrastive Learning for Long-Tailed Semi-Supervised Recognition","date":"2024-10-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"zhouzihao11/CCL","path":"dataset/cifar.py","file_url":"https://github.com/zhouzihao11/CCL/blob/HEAD/dataset/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"7b18bf51896a5c3f","mcp_get_code":{"code_sha256":"7b18bf51896a5c3f"}},{"arxiv_id":"2403.10391","paper":"/paper/cdmad-class-distribution-mismatch-aware","title":"CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeeHyuck/CDMAD","path":"dataset/fix_cifar10.py","file_url":"https://github.com/LeeHyuck/CDMAD/blob/HEAD/dataset/fix_cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3d7953151d774d75","mcp_get_code":{"code_sha256":"3d7953151d774d75"}},{"arxiv_id":"2403.10391","paper":"/paper/cdmad-class-distribution-mismatch-aware","title":"CDMAD: Class-Distribution-Mismatch-Aware Debiasing for Class-Imbalanced Semi-Supervised Learning","date":"2024-03-15","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"LeeHyuck/CDMAD","path":"dataset/remix_cifar10.py","file_url":"https://github.com/LeeHyuck/CDMAD/blob/HEAD/dataset/remix_cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"5a1d3cd7b6f8dc4e","mcp_get_code":{"code_sha256":"5a1d3cd7b6f8dc4e"}},{"arxiv_id":"2402.13505","paper":"/paper/simpro-a-simple-probabilistic-framework","title":"SimPro: A Simple Probabilistic Framework Towards Realistic Long-Tailed Semi-Supervised Learning","date":"2024-02-21","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"leaplabthu/simpro","path":"SimPro/dataset/cifar.py","file_url":"https://github.com/leaplabthu/simpro/blob/HEAD/SimPro/dataset/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"681a084b06d36aee","mcp_get_code":{"code_sha256":"681a084b06d36aee"}},{"arxiv_id":"2312.12703","paper":"/paper/federated-learning-with-extremely-noisy","title":"Federated Learning with Extremely Noisy Clients via Negative Distillation","date":"2023-12-20","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"linchen99/fedned","path":"dataset/get_cifar10.py","file_url":"https://github.com/linchen99/fedned/blob/HEAD/dataset/get_cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"3e4ccc9c4bfaf8a8","mcp_get_code":{"code_sha256":"3e4ccc9c4bfaf8a8"}},{"arxiv_id":"2310.04078","paper":"/paper/beyond-myopia-learning-from-positive-and-1","title":"Beyond Myopia: Learning from Positive and Unlabeled Data through Holistic Predictive Trends","date":"2023-10-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"wxr99/holisticpu","path":"dataset/cifar.py","file_url":"https://github.com/wxr99/holisticpu/blob/HEAD/dataset/cifar.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"code_sha256_prefix":"faace660b8d96dbc","mcp_get_code":{"code_sha256":"faace660b8d96dbc"}},{"arxiv_id":"2308.16258","paper":"/paper/robust-principles-architectural-design","title":"Robust Principles: Architectural Design Principles for Adversarially Robust CNNs","date":"2023-08-30","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"poloclub/robust-principles","path":"robustarch/utils.py","file_url":"https://github.com/poloclub/robust-principles/blob/HEAD/robustarch/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"782bf0b123fbfb08","mcp_get_code":{"code_sha256":"782bf0b123fbfb08"}},{"arxiv_id":"2301.03110","paper":"/paper/robarch-designing-robust-architectures","title":"RobArch: Designing Robust Architectures against Adversarial Attacks","date":"2023-01-08","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"shengyun-peng/robarch","path":"robustarch/utils.py","file_url":"https://github.com/shengyun-peng/robarch/blob/HEAD/robustarch/utils.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"782bf0b123fbfb08","mcp_get_code":{"code_sha256":"782bf0b123fbfb08"}},{"arxiv_id":"2202.08132","paper":"/paper/prospect-pruning-finding-trainable-weights-at-1","title":"Prospect Pruning: Finding Trainable Weights at Initialization using Meta-Gradients","date":"2022-02-16","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"mil-ad/prospr","path":"datasets.py","file_url":"https://github.com/mil-ad/prospr/blob/HEAD/datasets.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"035a3c7ada3cd3eb","mcp_get_code":{"code_sha256":"035a3c7ada3cd3eb"}},{"arxiv_id":"2202.04557","paper":"/paper/universal-hopfield-networks-a-general","title":"Universal Hopfield Networks: A General Framework for Single-Shot Associative Memory Models","date":"2022-02-09","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"BerenMillidge/Theory_Associative_Memory","path":"data.py","file_url":"https://github.com/BerenMillidge/Theory_Associative_Memory/blob/HEAD/data.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"53ce2056552aef4f","mcp_get_code":{"code_sha256":"53ce2056552aef4f"}},{"arxiv_id":"2003.14297","paper":"/paper/learning-from-small-data-through-sampling-an","title":"Generative Latent Implicit Conditional Optimization when Learning from Small Sample","date":"2020-03-31","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"IdanAzuri/glico-learning-small-sample","path":"glico_model/cifar10.py","file_url":"https://github.com/IdanAzuri/glico-learning-small-sample/blob/HEAD/glico_model/cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"492e6a1a39e01db2","mcp_get_code":{"code_sha256":"492e6a1a39e01db2"}},{"arxiv_id":"1905.02249","paper":"/paper/mixmatch-a-holistic-approach-to-semi","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"kevinghst/mixmatch","path":"dataset/cifar10.py","file_url":"https://github.com/kevinghst/mixmatch/blob/HEAD/dataset/cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"a0e25dd8565d9d94","mcp_get_code":{"code_sha256":"a0e25dd8565d9d94"}},{"arxiv_id":"1905.02249","paper":"/paper/mixmatch-a-holistic-approach-to-semi","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","date":"2019-05-06","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"narendoraiswamy/MixMatch-pytorch-demo","path":"dataset/_cifar10.py","file_url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo/blob/HEAD/dataset/_cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"0f1e6cdf5451e58d","mcp_get_code":{"code_sha256":"0f1e6cdf5451e58d"}},{"arxiv_id":"1810.12042","paper":"/paper/logit-pairing-methods-can-fool-gradient-based","title":"Logit Pairing Methods Can Fool Gradient-Based Attacks","date":"2018-10-29","month_inferred_from_arxiv_id":null,"title_source":"archive","repo":"uds-lsv/evaluating-logit-pairing-methods","path":"mnist_cifar10/data_loader.py","file_url":"https://github.com/uds-lsv/evaluating-logit-pairing-methods/blob/HEAD/mnist_cifar10/data_loader.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":"04ff1339b08bf76e","mcp_get_code":{"code_sha256":"04ff1339b08bf76e"}},{"arxiv_id":"Quetu_LaCoOT_Layer_Collapse_through_Optimal_Transport_ICCV_2025_paper","paper":null,"title":"arXiv:Quetu_LaCoOT_Layer_Collapse_through_Optimal_Transport_ICCV_2025_paper","date":null,"month_inferred_from_arxiv_id":null,"title_source":null,"repo":"VGCQ/LaCoOT","path":"dataloaders/cifar10.py","file_url":"https://github.com/VGCQ/LaCoOT/blob/HEAD/dataloaders/cifar10.py","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"code_sha256_prefix":"2da4f07a3351a751","mcp_get_code":{"code_sha256":"2da4f07a3351a751"}}]}