{"url":"/task/semi-supervised-image-classification","name":"Semi-Supervised Image Classification","slug":"semi-supervised-image-classification","description_markdown":"Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.\r\n\r\nYou may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:\r\n\r\n- [An overview of proxy-label approaches for semi-supervised learning](https://ruder.io/semi-supervised/) - Sebastian Ruder\r\n- [Semi-Supervised Learning in Computer Vision](https://amitness.com/2020/07/semi-supervised-learning/) - Amit Chaudhary\r\n\r\n<span style=\"color:grey; opacity: 0.6\">( Image credit: [Self-Supervised Semi-Supervised Learning](https://arxiv.org/pdf/1905.03670v2.pdf) )</span>","categories":[{"name":"Computer Vision","url":"/area/computer-vision"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":167,"papers_with_code":130,"benchmarks":58,"benchmark_tables_in_archive":60,"benchmark_tables_shown":60,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":14,"subtasks":2,"parent_tasks":1},"benchmarks":[{"leaderboard":"/sota/semi-supervised-image-classification-on-2","slug":"semi-supervised-image-classification-on-2","dataset":"ImageNet - 10% labeled data","dataset_url":"/dataset/imagenet","rows_in_archive":75,"metrics":["Top 1 Accuracy","Top 5 Accuracy","Number of params"],"first_row_in_archive_order":{"model":"DHO (ViT-Large)","paper_title":"Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\\mathbf{\\texttt{D}}$ual-$\\mathbf{\\texttt{H}}$ead $\\mathbf{\\texttt{O}}$ptimization","paper_url":"/paper/simple-semi-supervised-knowledge-distillation","paper_date":"2025-05-12","arxiv_id":"2505.07675","code_links":[{"title":"erjui/DHO","url":"https://github.com/erjui/DHO"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-1","slug":"semi-supervised-image-classification-on-1","dataset":"ImageNet - 1% labeled data","dataset_url":"/dataset/imagenet","rows_in_archive":65,"metrics":["Top 1 Accuracy","Top 5 Accuracy","Number of params"],"first_row_in_archive_order":{"model":"DHO (ViT-Large)","paper_title":"Simple Semi-supervised Knowledge Distillation from Vision-Language Models via $\\mathbf{\\texttt{D}}$ual-$\\mathbf{\\texttt{H}}$ead $\\mathbf{\\texttt{O}}$ptimization","paper_url":"/paper/simple-semi-supervised-knowledge-distillation","paper_date":"2025-05-12","arxiv_id":"2505.07675","code_links":[{"title":"erjui/DHO","url":"https://github.com/erjui/DHO"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar","slug":"semi-supervised-image-classification-on-cifar","dataset":"CIFAR-10, 4000 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":49,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"Semi-SST (ViT-Small)","paper_title":"SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning","paper_url":"/paper/sst-self-training-with-self-adaptive-1","paper_date":"2025-05-31","arxiv_id":"2506.00467","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-2","slug":"semi-supervised-image-classification-on-cifar-2","dataset":"cifar-100, 10000 Labels","dataset_url":"/dataset/cifar-100","rows_in_archive":29,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"Semi-SST (ViT-Small)","paper_title":"SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning","paper_url":"/paper/sst-self-training-with-self-adaptive-1","paper_date":"2025-05-31","arxiv_id":"2506.00467","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-6","slug":"semi-supervised-image-classification-on-cifar-6","dataset":"CIFAR-10, 250 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":27,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"Semi-SST (ViT-Small)","paper_title":"SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning","paper_url":"/paper/sst-self-training-with-self-adaptive-1","paper_date":"2025-05-31","arxiv_id":"2506.00467","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-7","slug":"semi-supervised-image-classification-on-cifar-7","dataset":"CIFAR-10, 40 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":21,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SemiOccam","paper_title":"ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels","paper_url":"/paper/vitsgmm-a-robust-semi-supervised-image-1","paper_date":"2025-06-04","arxiv_id":"2506.03582","code_links":[{"title":"Shu1L0n9/SemiOccam","url":"https://github.com/Shu1L0n9/SemiOccam"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-8","slug":"semi-supervised-image-classification-on-cifar-8","dataset":"CIFAR-100, 400 Labels","dataset_url":"/dataset/cifar-100","rows_in_archive":21,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SemiReward","paper_title":"SemiReward: A General Reward Model for Semi-supervised Learning","paper_url":"/paper/semireward-a-general-reward-model-for-semi","paper_date":"2023-10-04","arxiv_id":"2310.03013","code_links":[{"title":"Westlake-AI/SemiReward","url":"https://github.com/Westlake-AI/SemiReward"}],"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":1}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn","slug":"semi-supervised-image-classification-on-svhn","dataset":"SVHN, 1000 labels","dataset_url":"/dataset/svhn","rows_in_archive":17,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Meta Pseudo Labels (WRN-28-2)","paper_title":"Meta Pseudo Labels","paper_url":"/paper/meta-pseudo-labels","paper_date":"2020-03-23","arxiv_id":"2003.10580","code_links":[{"title":"google-research/google-research","url":"https://github.com/google-research/google-research/tree/master/meta_pseudo_labels"},{"title":"kekmodel/MPL-pytorch","url":"https://github.com/kekmodel/MPL-pytorch"},{"title":"sayakpaul/PAWS-TF","url":"https://github.com/sayakpaul/PAWS-TF"},{"title":"ifsheldon/MPL_Lightning","url":"https://github.com/ifsheldon/MPL_Lightning"},{"title":"retoschiegg/meta-pseudo-labels","url":"https://github.com/retoschiegg/meta-pseudo-labels"},{"title":"usccolumbia/tsdnn","url":"https://github.com/usccolumbia/tsdnn"},{"title":"YanYan0716/MPL","url":"https://github.com/YanYan0716/MPL"},{"title":"ve450su2021-group26/Algorithm","url":"https://github.com/ve450su2021-group26/Algorithm"},{"title":"7ZFG1/Semi-Supervised-Learning-Meta-Pseudo-Learning-","url":"https://github.com/7ZFG1/Semi-Supervised-Learning-Meta-Pseudo-Learning-"}],"syntology":{"n":14,"n_ran":5,"n_unverified":9,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-9","slug":"semi-supervised-image-classification-on-cifar-9","dataset":"CIFAR-100, 2500 Labels","dataset_url":"/dataset/cifar-100","rows_in_archive":16,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"Semi-SST (ViT-Small)","paper_title":"SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning","paper_url":"/paper/sst-self-training-with-self-adaptive-1","paper_date":"2025-05-31","arxiv_id":"2506.00467","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-1","slug":"semi-supervised-image-classification-on-svhn-1","dataset":"SVHN, 250 Labels","dataset_url":"/dataset/svhn","rows_in_archive":15,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"ShrinkMatch","paper_title":"Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning","paper_url":"/paper/shrinking-class-space-for-enhanced-certainty","paper_date":"2023-08-13","arxiv_id":"2308.06777","code_links":[{"title":"LiheYoung/ShrinkMatch","url":"https://github.com/LiheYoung/ShrinkMatch"}],"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-1","slug":"semi-supervised-image-classification-on-stl-1","dataset":"STL-10, 1000 Labels","dataset_url":null,"rows_in_archive":13,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Diff-SySC","paper_title":"Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification","paper_url":"/paper/diff-sysc-an-approach-using-diffusion-models","paper_date":"2025-02-25","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-11","slug":"semi-supervised-image-classification-on-cifar-11","dataset":"CIFAR-10, 1000 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":9,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-3","slug":"semi-supervised-image-classification-on-svhn-3","dataset":"SVHN, 500 Labels","dataset_url":"/dataset/svhn","rows_in_archive":6,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"Triple-GAN-V2 (CNN-13)","paper_title":"Triple Generative Adversarial Networks","paper_url":"/paper/triple-generative-adversarial-networks","paper_date":"2019-12-20","arxiv_id":"1912.09784","code_links":[{"title":"taufikxu/Triple-GAN","url":"https://github.com/taufikxu/Triple-GAN"}],"syntology":{"n":8,"n_ran":0,"n_unverified":8,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-2","slug":"semi-supervised-image-classification-on-svhn-2","dataset":"SVHN, 40 Labels","dataset_url":"/dataset/svhn","rows_in_archive":5,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"ShrinkMatch","paper_title":"Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning","paper_url":"/paper/shrinking-class-space-for-enhanced-certainty","paper_date":"2023-08-13","arxiv_id":"2308.06777","code_links":[{"title":"LiheYoung/ShrinkMatch","url":"https://github.com/LiheYoung/ShrinkMatch"}],"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-3","slug":"semi-supervised-image-classification-on-3","dataset":"cifar10, 250 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":4,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"ReMixMatch","paper_title":"ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring","paper_url":"/paper/remixmatch-semi-supervised-learning-with-1","paper_date":"2019-11-21","arxiv_id":"1911.09785","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"google-research/remixmatch","url":"https://github.com/google-research/remixmatch"},{"title":"zysymu/AdaMatch-pytorch","url":"https://github.com/zysymu/AdaMatch-pytorch"}],"syntology":{"n":4,"n_ran":1,"n_unverified":3,"n_pointer_only":1}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-12","slug":"semi-supervised-image-classification-on-cifar-12","dataset":"CIFAR-10, 2000 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-36","slug":"semi-supervised-image-classification-on-cifar-36","dataset":"CIFAR-10, 50 Labels (OpenSet, 6/4)","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-37","slug":"semi-supervised-image-classification-on-cifar-37","dataset":"CIFAR-10, 100 Labels (OpenSet, 6/4)","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-38","slug":"semi-supervised-image-classification-on-cifar-38","dataset":"CIFAR-10, 400 Labels (OpenSet, 6/4)","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-mini","slug":"semi-supervised-image-classification-on-mini","dataset":"Mini-ImageNet, 4000 Labels","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SimPLE","paper_title":"SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification","paper_url":"/paper/simple-similar-pseudo-label-exploitation-for","paper_date":"2021-03-30","arxiv_id":"2103.16725","code_links":[{"title":"zijian-hu/SimPLE","url":"https://github.com/zijian-hu/SimPLE"}],"syntology":{"n":5,"n_ran":3,"n_unverified":2,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-3","slug":"semi-supervised-image-classification-on-stl-3","dataset":"STL-10, 40 Labels","dataset_url":null,"rows_in_archive":4,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"SemiOccam","paper_title":"ViTSGMM: A Robust Semi-Supervised Image Recognition Network Using Sparse Labels","paper_url":"/paper/vitsgmm-a-robust-semi-supervised-image-1","paper_date":"2025-06-04","arxiv_id":"2506.03582","code_links":[{"title":"Shu1L0n9/SemiOccam","url":"https://github.com/Shu1L0n9/SemiOccam"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-16","slug":"semi-supervised-image-classification-on-16","dataset":"ImageNet - 0.2% labeled data","dataset_url":"/dataset/imagenet","rows_in_archive":3,"metrics":["ImageNet Top-1 Accuracy"],"first_row_in_archive_order":{"model":"DebiasPL (ResNet-50)","paper_title":"Debiased Learning from Naturally Imbalanced Pseudo-Labels","paper_url":"/paper/debiased-learning-from-naturally-imbalanced","paper_date":"2022-01-05","arxiv_id":"2201.01490","code_links":[{"title":"frank-xwang/debiased-pseudo-labeling","url":"https://github.com/frank-xwang/debiased-pseudo-labeling"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-15","slug":"semi-supervised-image-classification-on-cifar-15","dataset":"CIFAR-10, 20 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":3,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"MutexMatch (k=0.6C)","paper_title":"MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization","paper_url":"/paper/mutexmatch-semi-supervised-learning-with-1","paper_date":"2022-03-27","arxiv_id":"2203.14316","code_links":[{"title":"NJUyued/MutexMatch4SSL","url":"https://github.com/NJUyued/MutexMatch4SSL"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-17","slug":"semi-supervised-image-classification-on-cifar-17","dataset":"cifar-10, 10 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":3,"metrics":["Accuracy (Test)"],"first_row_in_archive_order":{"model":"BOSS","paper_title":"Building One-Shot Semi-supervised (BOSS) Learning up to Fully Supervised Performance","paper_url":"/paper/building-one-shot-semi-supervised-boss","paper_date":"2020-06-16","arxiv_id":"2006.09363","code_links":[{"title":"lnsmith54/BOSS","url":"https://github.com/lnsmith54/BOSS"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-mini-1","slug":"semi-supervised-image-classification-on-mini-1","dataset":"Mini-ImageNet, 10000 Labels","dataset_url":null,"rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"FeatMatch","paper_title":"FeatMatch: Feature-Based Augmentation for Semi-Supervised Learning","paper_url":"/paper/featmatch-feature-based-augmentation-for-semi","paper_date":"2020-07-16","arxiv_id":"2007.08505","code_links":[{"title":"GT-RIPL/FeatMatch","url":"https://github.com/GT-RIPL/FeatMatch"},{"title":"JiwonCocoder/label_transformer","url":"https://github.com/JiwonCocoder/label_transformer"}],"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":1}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-mini-2","slug":"semi-supervised-image-classification-on-mini-2","dataset":"Mini-ImageNet, 1000 Labels","dataset_url":null,"rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MutexMatch","paper_title":"MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization","paper_url":"/paper/mutexmatch-semi-supervised-learning-with-1","paper_date":"2022-03-27","arxiv_id":"2203.14316","code_links":[{"title":"NJUyued/MutexMatch4SSL","url":"https://github.com/NJUyued/MutexMatch4SSL"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl","slug":"semi-supervised-image-classification-on-stl","dataset":"STL-10","dataset_url":"/dataset/stl-10","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"EnAET","paper_title":"EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations","paper_url":"/paper/enaet-self-trained-ensemble-autoencoding","paper_date":"2019-11-21","arxiv_id":"1911.09265","code_links":[{"title":"maple-research-lab/EnAET","url":"https://github.com/maple-research-lab/EnAET"},{"title":"wang3702/EnAET","url":"https://github.com/wang3702/EnAET"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-16","slug":"semi-supervised-image-classification-on-cifar-16","dataset":"CIFAR-10, 80 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":2,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"MutexMatch (k=0.6C)","paper_title":"MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization","paper_url":"/paper/mutexmatch-semi-supervised-learning-with-1","paper_date":"2022-03-27","arxiv_id":"2203.14316","code_links":[{"title":"NJUyued/MutexMatch4SSL","url":"https://github.com/NJUyued/MutexMatch4SSL"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-29","slug":"semi-supervised-image-classification-on-cifar-29","dataset":"CIFAR-100 (400 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-30","slug":"semi-supervised-image-classification-on-cifar-30","dataset":"CIFAR-100 (250 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuarcy"],"first_row_in_archive_order":{"model":"CCSSL","paper_title":"Class-Aware Contrastive Semi-Supervised Learning","paper_url":"/paper/class-aware-contrastive-semi-supervised","paper_date":"2022-03-04","arxiv_id":"2203.02261","code_links":[{"title":"tencentyouturesearch/classification-semicls","url":"https://github.com/tencentyouturesearch/classification-semicls"}],"syntology":{"n":3,"n_ran":2,"n_unverified":1,"n_pointer_only":3}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-33","slug":"semi-supervised-image-classification-on-cifar-33","dataset":"CIFAR-100 (10000 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-34","slug":"semi-supervised-image-classification-on-cifar-34","dataset":"CIFAR-10 (250 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-35","slug":"semi-supervised-image-classification-on-cifar-35","dataset":"CIFAR-10 (4000 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-4","slug":"semi-supervised-image-classification-on-cifar-4","dataset":"CIFAR-100, 5000Labels","dataset_url":"/dataset/cifar-100","rows_in_archive":2,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"LiDAM","paper_title":"LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching","paper_url":"/paper/lidam-semi-supervised-learning-with-localized","paper_date":"2020-10-13","arxiv_id":"2010.06668","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-5","slug":"semi-supervised-image-classification-on-stl-5","dataset":"STL-10 (1000 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-7","slug":"semi-supervised-image-classification-on-svhn-7","dataset":"SVHN (250 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-8","slug":"semi-supervised-image-classification-on-svhn-8","dataset":"SVHN (40 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-9","slug":"semi-supervised-image-classification-on-svhn-9","dataset":"SVHN (1000 Labels, ImageNet-100 Unlabeled)","dataset_url":null,"rows_in_archive":2,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UnMixMatch","paper_title":"Scaling Up Semi-supervised Learning with Unconstrained Unlabelled Data","paper_url":"/paper/scaling-up-semi-supervised-learning-with","paper_date":"2023-06-02","arxiv_id":"2306.01222","code_links":[{"title":"shuvenduroy/unmixmatch","url":"https://github.com/shuvenduroy/unmixmatch"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-10","slug":"semi-supervised-image-classification-on-10","dataset":"Caltech-256, 1024 Labels","dataset_url":"/dataset/caltech-256","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper_title":"Unsupervised Learning using Pretrained CNN and Associative Memory Bank","paper_url":"/paper/unsupervised-learning-using-pretrained-cnn","paper_date":"2018-05-02","arxiv_id":"1805.01033","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-11","slug":"semi-supervised-image-classification-on-11","dataset":"Caltech-256","dataset_url":"/dataset/caltech-256","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper_title":"Unsupervised Learning using Pretrained CNN and Associative Memory Bank","paper_url":"/paper/unsupervised-learning-using-pretrained-cnn","paper_date":"2018-05-02","arxiv_id":"1805.01033","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-17","slug":"semi-supervised-image-classification-on-17","dataset":"Salinas","dataset_url":"/dataset/salinas","rows_in_archive":1,"metrics":["Overall Accuracy"],"first_row_in_archive_order":{"model":"Res-CP","paper_title":"Semi-Supervised Hyperspectral Image Classification Using a Probabilistic Pseudo-Label Generation Framework","paper_url":"/paper/semi-supervised-hyperspectral-image-1","paper_date":"2022-08-05","arxiv_id":null,"code_links":[{"title":"majidseydgar/Res-CP","url":"https://github.com/majidseydgar/Res-CP"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-21","slug":"semi-supervised-image-classification-on-21","dataset":"EuroSAT, 20 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-22","slug":"semi-supervised-image-classification-on-22","dataset":"Imagenette, 20 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-23","slug":"semi-supervised-image-classification-on-23","dataset":"Imagenette, 100 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-24","slug":"semi-supervised-image-classification-on-24","dataset":"EuroSAT, 100 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-25","slug":"semi-supervised-image-classification-on-25","dataset":"DeepWeeds, 99 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-finetuned","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-8","slug":"semi-supervised-image-classification-on-8","dataset":"Caltech-101","dataset_url":"/dataset/caltech-101","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper_title":"Unsupervised Learning using Pretrained CNN and Associative Memory Bank","paper_url":"/paper/unsupervised-learning-using-pretrained-cnn","paper_date":"2018-05-02","arxiv_id":"1805.01033","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-9","slug":"semi-supervised-image-classification-on-9","dataset":"Caltech-101, 202 Labels","dataset_url":"/dataset/caltech-101","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UL-Hopfield (ULH)","paper_title":"Unsupervised Learning using Pretrained CNN and Associative Memory Bank","paper_url":"/paper/unsupervised-learning-using-pretrained-cnn","paper_date":"2018-05-02","arxiv_id":"1805.01033","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-10","slug":"semi-supervised-image-classification-on-cifar-10","dataset":"CIFAR-10, 500 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-20","slug":"semi-supervised-image-classification-on-cifar-20","dataset":"CIFAR-100, 4000 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"UPS (CNN-13)","paper_title":"In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning","paper_url":"/paper/in-defense-of-pseudo-labeling-an-uncertainty-1","paper_date":"2021-01-15","arxiv_id":"2101.06329","code_links":[{"title":"nayeemrizve/ups","url":"https://github.com/nayeemrizve/ups"},{"title":"matinmoezzi/ups_conformal_classification","url":"https://github.com/matinmoezzi/ups_conformal_classification"}],"syntology":{"n":5,"n_ran":0,"n_unverified":5,"n_pointer_only":3}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-24","slug":"semi-supervised-image-classification-on-cifar-24","dataset":"CIFAR-100, 5000 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy (%)"],"first_row_in_archive_order":{"model":"LiDAM","paper_title":"LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching","paper_url":"/paper/lidam-semi-supervised-learning-with-localized","paper_date":"2020-10-13","arxiv_id":"2010.06668","code_links":[],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-25","slug":"semi-supervised-image-classification-on-cifar-25","dataset":"CIFAR-100, 200 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"MutexMatch (k=0.6C)","paper_title":"MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization","paper_url":"/paper/mutexmatch-semi-supervised-learning-with-1","paper_date":"2022-03-27","arxiv_id":"2203.14316","code_links":[{"title":"NJUyued/MutexMatch4SSL","url":"https://github.com/NJUyued/MutexMatch4SSL"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-27","slug":"semi-supervised-image-classification-on-cifar-27","dataset":"CIFAR-10, 100 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-28","slug":"semi-supervised-image-classification-on-cifar-28","dataset":"CIFAR-10, 30 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":1,"metrics":["Percentage error"],"first_row_in_archive_order":{"model":"SimCLR-kmediods-PAWS","paper_title":"Cold PAWS: Unsupervised class discovery and addressing the cold-start problem for semi-supervised learning","paper_url":"/paper/cold-paws-unsupervised-class-discovery-and-1","paper_date":"2023-05-17","arxiv_id":"2305.10071","code_links":[{"title":"emannix/cold-paws-simclr-and-paws-semi-supervised-learning","url":"https://github.com/emannix/cold-paws-simclr-and-paws-semi-supervised-learning"},{"title":"emannix/cold-paws-labelling-selection-strategies","url":"https://github.com/emannix/cold-paws-labelling-selection-strategies"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-cifar-3","slug":"semi-supervised-image-classification-on-cifar-3","dataset":"CIFAR-100, 1000 Labels","dataset_url":"/dataset/cifar-100","rows_in_archive":1,"metrics":["Percentage correct"],"first_row_in_archive_order":{"model":"EnAET","paper_title":"EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations","paper_url":"/paper/enaet-self-trained-ensemble-autoencoding","paper_date":"2019-11-21","arxiv_id":"1911.09265","code_links":[{"title":"maple-research-lab/EnAET","url":"https://github.com/maple-research-lab/EnAET"},{"title":"wang3702/EnAET","url":"https://github.com/wang3702/EnAET"}],"syntology":null}},{"leaderboard":"/sota/semi-supervised-image-classification-on-stl-2","slug":"semi-supervised-image-classification-on-stl-2","dataset":"STL-10, 5000 Labels","dataset_url":null,"rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-4","slug":"semi-supervised-image-classification-on-svhn-4","dataset":"SVHN, 2000 Labels","dataset_url":"/dataset/svhn","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":"/sota/semi-supervised-image-classification-on-svhn-5","slug":"semi-supervised-image-classification-on-svhn-5","dataset":"SVHN, 4000 Labels","dataset_url":"/dataset/svhn","rows_in_archive":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"MixMatch","paper_title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","paper_url":"/paper/mixmatch-a-holistic-approach-to-semi","paper_date":"2019-05-06","arxiv_id":"1905.02249","code_links":[{"title":"google-research/mixmatch","url":"https://github.com/google-research/mixmatch"},{"title":"YU1ut/MixMatch-pytorch","url":"https://github.com/YU1ut/MixMatch-pytorch"},{"title":"gan3sh500/mixmatch-pytorch","url":"https://github.com/gan3sh500/mixmatch-pytorch"},{"title":"google-research/crest","url":"https://github.com/google-research/crest"},{"title":"perrying/realistic-ssl-evaluation-pytorch","url":"https://github.com/perrying/realistic-ssl-evaluation-pytorch"},{"title":"uizard-technologies/realmix","url":"https://github.com/uizard-technologies/realmix"},{"title":"rit-git/Snippext_public","url":"https://github.com/rit-git/Snippext_public"},{"title":"FelixAbrahamsson/mixmatch-pytorch","url":"https://github.com/FelixAbrahamsson/mixmatch-pytorch"},{"title":"filaPro/visda2019","url":"https://github.com/filaPro/visda2019"},{"title":"noachr/MixMatch-fastai","url":"https://github.com/noachr/MixMatch-fastai"},{"title":"TianheWu/LGPNet","url":"https://github.com/TianheWu/LGPNet"},{"title":"Jeffkang-94/Mixmatch-pytorch-SSL","url":"https://github.com/Jeffkang-94/Mixmatch-pytorch-SSL"},{"title":"Jeffkang-94/pytorch-MixMatch","url":"https://github.com/Jeffkang-94/pytorch-MixMatch"},{"title":"yuxi120407/mixmatch_tensorflow","url":"https://github.com/yuxi120407/mixmatch_tensorflow"},{"title":"ntozer/mixmatch-tensorflow2.0","url":"https://github.com/ntozer/mixmatch-tensorflow2.0"},{"title":"yuxi120407/semi-supervised_tensorflow2.0","url":"https://github.com/yuxi120407/semi-supervised_tensorflow2.0"},{"title":"dLutscher/MixMatch-TransferLearning","url":"https://github.com/dLutscher/MixMatch-TransferLearning"},{"title":"Shubhammawa/MixMatch-Semi-Supervised-Learning","url":"https://github.com/Shubhammawa/MixMatch-Semi-Supervised-Learning"},{"title":"viig99/mixmatch-freesound","url":"https://github.com/viig99/mixmatch-freesound"},{"title":"smkim7-kr/albu-MixMatch-pytorch","url":"https://github.com/smkim7-kr/albu-MixMatch-pytorch"},{"title":"fbuchert/mixmatch-pytorch","url":"https://github.com/fbuchert/mixmatch-pytorch"},{"title":"atinghosh/mixmatch_pytorch","url":"https://github.com/atinghosh/mixmatch_pytorch"},{"title":"tinluu/Self-supervised-CV","url":"https://github.com/tinluu/Self-supervised-CV"},{"title":"DamianValle/ScarceClassifier","url":"https://github.com/DamianValle/ScarceClassifier"},{"title":"dhx000/DGM_project","url":"https://github.com/dhx000/DGM_project"},{"title":"ms903-github/MixMatch-imdb","url":"https://github.com/ms903-github/MixMatch-imdb"},{"title":"kevinghst/mixmatch","url":"https://github.com/kevinghst/mixmatch"},{"title":"narendoraiswamy/MixMatch-pytorch-demo","url":"https://github.com/narendoraiswamy/MixMatch-pytorch-demo"},{"title":"ktran1/Manifold-attack","url":"https://github.com/ktran1/Manifold-attack"},{"title":"DonghwanKIM0101/CS492I_CV","url":"https://github.com/DonghwanKIM0101/CS492I_CV"}],"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}}},{"leaderboard":null,"slug":"semi-supervised-image-classification-on-27","dataset":"ImageNet","dataset_url":"/dataset/imagenet","rows_in_archive":0,"metrics":["Accuracy at 1%"],"first_row_in_archive_order":null},{"leaderboard":null,"slug":"semi-supervised-image-classification-on-cifar-39","dataset":"CIFAR-10, 40 Labels","dataset_url":"/dataset/cifar-10","rows_in_archive":0,"metrics":["Percentage error"],"first_row_in_archive_order":null}],"datasets":[{"url":"/dataset/cifar-10","name":"CIFAR-10","full_name":"CIFAR-10","num_papers_in_archive":16145},{"url":"/dataset/imagenet","name":"ImageNet","full_name":"","num_papers_in_archive":15430},{"url":"/dataset/cifar-100","name":"CIFAR-100","full_name":"","num_papers_in_archive":9045},{"url":"/dataset/svhn","name":"SVHN","full_name":"Street View House Numbers","num_papers_in_archive":3406},{"url":"/dataset/stl-10","name":"STL-10","full_name":"Self-Taught Learning 10","num_papers_in_archive":1092},{"url":"/dataset/caltech-101","name":"Caltech-101","full_name":"","num_papers_in_archive":709},{"url":"/dataset/caltech-256","name":"Caltech-256","full_name":"","num_papers_in_archive":401},{"url":"/dataset/salinas","name":"Salinas","full_name":"Salinas Scene","num_papers_in_archive":15},{"url":"/dataset/semi-inat","name":"Semi-iNat","full_name":"Semi-Supervised iNaturalist","num_papers_in_archive":12},{"url":"/dataset/tmed","name":"TMED","full_name":"Tufts Medical Echocardiogram Dataset","num_papers_in_archive":10},{"url":"/dataset/bioscan-5m","name":"BIOSCAN-5M","full_name":"","num_papers_in_archive":4},{"url":"/dataset/limuc","name":"LIMUC","full_name":"Labeled Images for Ulcerative Colitis","num_papers_in_archive":4},{"url":"/dataset/barknet-1-0","name":"BarkNet 1.0","full_name":"","num_papers_in_archive":3},{"url":"/dataset/herigraph","name":"HeriGraph","full_name":"Multimodal Machine Learning Datasets on Graphs of Heritage Values and Attributes","num_papers_in_archive":2}],"subtasks":[{"url":"/task/open-world-semi-supervised-learning","name":"Open-World Semi-Supervised Learning"},{"url":"/task/semi-supervised-image-classification-cold","name":"Semi-Supervised Image Classification (Cold Start)"}],"parent_tasks":[{"url":"/task/image-classification","name":"Image Classification"}],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":130,"tagged_in_all":167,"items":[{"url":"/paper/a-simple-framework-for-contrastive-learning","title":"A Simple Framework for Contrastive Learning of Visual Representations","date":"2020-02-13","arxiv_id":"2002.05709","repositories_listed":96,"syntology":{"n":137,"n_ran":79,"n_unverified":58,"n_pointer_only":52}},{"url":"/paper/learning-transferable-visual-models-from","title":"Learning Transferable Visual Models From Natural Language Supervision","date":"2021-02-26","arxiv_id":"2103.00020","repositories_listed":82,"syntology":{"n":20,"n_ran":16,"n_unverified":4,"n_pointer_only":16}},{"url":"/paper/mixup-beyond-empirical-risk-minimization","title":"mixup: Beyond Empirical Risk Minimization","date":"2017-10-25","arxiv_id":"1710.09412","repositories_listed":71,"syntology":{"n":47,"n_ran":30,"n_unverified":17,"n_pointer_only":15}},{"url":"/paper/improved-techniques-for-training-gans","title":"Improved Techniques for Training GANs","date":"2016-06-10","arxiv_id":"1606.03498","repositories_listed":46,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/bootstrap-your-own-latent-a-new-approach-to","title":"Bootstrap your own latent: A new approach to self-supervised Learning","date":"2020-06-13","arxiv_id":"2006.07733","repositories_listed":31,"syntology":{"n":79,"n_ran":62,"n_unverified":17,"n_pointer_only":46}},{"url":"/paper/mixmatch-a-holistic-approach-to-semi","title":"MixMatch: A Holistic Approach to Semi-Supervised Learning","date":"2019-05-06","arxiv_id":"1905.02249","repositories_listed":30,"syntology":{"n":63,"n_ran":39,"n_unverified":24,"n_pointer_only":32}},{"url":"/paper/representation-learning-with-contrastive","title":"Representation Learning with Contrastive Predictive Coding","date":"2018-07-10","arxiv_id":"1807.03748","repositories_listed":28,"syntology":{"n":45,"n_ran":29,"n_unverified":16,"n_pointer_only":22}},{"url":"/paper/improved-regularization-of-convolutional","title":"Improved Regularization of Convolutional Neural Networks with Cutout","date":"2017-08-15","arxiv_id":"1708.04552","repositories_listed":28,"syntology":{"n":24,"n_ran":21,"n_unverified":3,"n_pointer_only":5}},{"url":"/paper/fixmatch-simplifying-semi-supervised-learning","title":"FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence","date":"2020-01-21","arxiv_id":"2001.07685","repositories_listed":26,"syntology":{"n":74,"n_ran":50,"n_unverified":24,"n_pointer_only":14}},{"url":"/paper/barlow-twins-self-supervised-learning-via","title":"Barlow Twins: Self-Supervised Learning via Redundancy Reduction","date":"2021-03-04","arxiv_id":"2103.03230","repositories_listed":24,"syntology":{"n":26,"n_ran":21,"n_unverified":5,"n_pointer_only":10}},{"url":"/paper/unsupervised-data-augmentation-1","title":"Unsupervised Data Augmentation for Consistency Training","date":"2019-04-29","arxiv_id":"1904.12848","repositories_listed":20,"syntology":{"n":52,"n_ran":15,"n_unverified":37,"n_pointer_only":9}},{"url":"/paper/unsupervised-learning-of-visual-features-by","title":"Unsupervised Learning of Visual Features by Contrasting Cluster Assignments","date":"2020-06-17","arxiv_id":"2006.09882","repositories_listed":18,"syntology":{"n":17,"n_ran":13,"n_unverified":4,"n_pointer_only":6}},{"url":"/paper/virtual-adversarial-training-a-regularization","title":"Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning","date":"2017-04-13","arxiv_id":"1704.03976","repositories_listed":14,"syntology":{"n":13,"n_ran":11,"n_unverified":2,"n_pointer_only":11}},{"url":"/paper/semi-supervised-learning-with-ladder-networks","title":"Semi-Supervised Learning with Ladder Networks","date":"2015-07-09","arxiv_id":"1507.02672","repositories_listed":10,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/big-self-supervised-models-are-strong-semi","title":"Big Self-Supervised Models are Strong Semi-Supervised Learners","date":"2020-06-17","arxiv_id":"2006.10029","repositories_listed":9,"syntology":{"n":6,"n_ran":0,"n_unverified":6,"n_pointer_only":0}},{"url":"/paper/meta-pseudo-labels","title":"Meta Pseudo Labels","date":"2020-03-23","arxiv_id":"2003.10580","repositories_listed":9,"syntology":{"n":14,"n_ran":5,"n_unverified":9,"n_pointer_only":0}},{"url":"/paper/mean-teachers-are-better-role-models-weight","title":"Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results","date":"2017-03-06","arxiv_id":"1703.01780","repositories_listed":8,"syntology":{"n":6,"n_ran":6,"n_unverified":0,"n_pointer_only":6}},{"url":"/paper/self-supervised-learning-of-pretext-invariant","title":"Self-Supervised Learning of Pretext-Invariant Representations","date":"2019-12-04","arxiv_id":"1912.01991","repositories_listed":7,"syntology":{"n":4,"n_ran":3,"n_unverified":1,"n_pointer_only":4}},{"url":"/paper/semi-supervised-learning-with-context","title":"Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks","date":"2016-11-19","arxiv_id":"1611.06430","repositories_listed":7,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/temporal-ensembling-for-semi-supervised","title":"Temporal Ensembling for Semi-Supervised Learning","date":"2016-10-07","arxiv_id":"1610.02242","repositories_listed":7,"syntology":{"n":7,"n_ran":3,"n_unverified":4,"n_pointer_only":2}},{"url":"/paper/freematch-self-adaptive-thresholding-for-semi","title":"FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning","date":"2022-05-15","arxiv_id":"2205.07246","repositories_listed":6,"syntology":{"n":14,"n_ran":4,"n_unverified":10,"n_pointer_only":2}},{"url":"/paper/vicreg-variance-invariance-covariance","title":"VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning","date":"2021-05-11","arxiv_id":"2105.04906","repositories_listed":6,"syntology":{"n":17,"n_ran":11,"n_unverified":6,"n_pointer_only":1}},{"url":"/paper/usb-a-unified-semi-supervised-learning","title":"USB: A Unified Semi-supervised Learning Benchmark for Classification","date":"2022-08-12","arxiv_id":"2208.07204","repositories_listed":5,"syntology":{"n":9,"n_ran":9,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/with-a-little-help-from-my-friends-nearest","title":"With a Little Help from My Friends: Nearest-Neighbor Contrastive Learning of Visual Representations","date":"2021-04-29","arxiv_id":"2104.14548","repositories_listed":4,"syntology":{"n":5,"n_ran":4,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/semi-supervised-learning-of-visual-features","title":"Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples","date":"2021-04-28","arxiv_id":"2104.13963","repositories_listed":4,"syntology":{"n":21,"n_ran":7,"n_unverified":14,"n_pointer_only":3}},{"url":"/paper/large-scale-adversarial-representation","title":"Large Scale Adversarial Representation Learning","date":"2019-07-04","arxiv_id":"1907.02544","repositories_listed":4,"syntology":{"n":3,"n_ran":0,"n_unverified":3,"n_pointer_only":0}},{"url":"/paper/data-efficient-image-recognition-with","title":"Data-Efficient Image Recognition with Contrastive Predictive Coding","date":"2019-05-22","arxiv_id":"1905.09272","repositories_listed":4,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/interpolation-consistency-training-for-semi","title":"Interpolation Consistency Training for Semi-Supervised Learning","date":"2019-03-09","arxiv_id":"1903.03825","repositories_listed":4,"syntology":{"n":2,"n_ran":1,"n_unverified":1,"n_pointer_only":2}},{"url":"/paper/unsupervised-feature-learning-via-non-1","title":"Unsupervised Feature Learning via Non-Parametric Instance Discrimination","date":"2018-06-01","arxiv_id":null,"repositories_listed":4,"syntology":null},{"url":"/paper/online-bag-of-visual-words-generation-for","title":"OBoW: Online Bag-of-Visual-Words Generation for Self-Supervised Learning","date":"2020-12-21","arxiv_id":"2012.11552","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}}],"syntology_records":29,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","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)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}