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In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning

15 Jan 2021ICLR 2021 1arXiv:2101.06329archive 2025-07-28

Mamshad Nayeem Rizve, Kevin Duarte, Yogesh S Rawat, Mubarak Shah

The recent research in semi-supervised learning (SSL) is mostly dominated by consistency regularization based methods which achieve strong performance. However, they heavily rely on domain-specific data augmentations, which are not easy to generate for all data modalities. Pseudo-labeling (PL) is a general SSL approach that does not have this constraint but performs relatively poorly in its original formulation. We argue that PL underperforms due to the erroneous high confidence predictions from poorly calibrated models; these predictions generate many incorrect pseudo-labels, leading to noisy training. We propose an uncertainty-aware pseudo-label selection (UPS) framework which improves pseudo labeling accuracy by drastically reducing the amount of noise encountered in the training process. Furthermore, UPS generalizes the pseudo-labeling process, allowing for the creation of negative pseudo-labels; these negative pseudo-labels can be used for multi-label classification as well as negative learning to improve the single-label classification. We achieve strong performance when compared to recent SSL methods on the CIFAR-10 and CIFAR-100 datasets. Also, we demonstrate the versatility of our method on the video dataset UCF-101 and the multi-label dataset Pascal VOC.

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cnn13 nayeemrizve/ups/models/cnn13.py official repository unverified MIT (permissive) · b61c4c542a153e87 · report
pseudo_labeling nayeemrizve/ups/utils/pseudo_labeling_util.py official repository unverified MIT (permissive) · 58c42391c2ccd1ae · report
accuracy matinmoezzi/ups_conformal_classification/utils/pseudo_labeling_util.py community (archive-listed) unverified no licence file found · pointer only · f525d64dc7aa3516 · report
enable_dropout matinmoezzi/ups_conformal_classification/utils/pseudo_labeling_util.py community (archive-listed) unverified no licence file found · pointer only · 586ccfec218401dd · report
pseudo_labeling matinmoezzi/ups_conformal_classification/utils/pseudo_labeling_util.py community (archive-listed) unverified no licence file found · pointer only · b0e6dd249d2917c0 · report

Tasks

MUlTI-LABEL-ClASSIFICATIONMulti-Label ClassificationPseudo LabelSemi-Supervised Image ClassificationSemi-Supervised Video ClassificationSemi-supervised Medical Image Classification

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Semi-Supervised Image Classification CIFAR-10, 1000 Labels UPS (CNN-13) Accuracy 91.82 #2 of 9 Archive leaderboard report
Semi-Supervised Image Classification CIFAR-10, 4000 Labels UPS (Shake-Shake) Percentage error 4.86 #21 of 49 Archive leaderboard report
Semi-Supervised Image Classification CIFAR-10, 4000 Labels UPS (CNN-13) Percentage error 6.39±0.02 #33 of 49 Archive leaderboard report
Semi-Supervised Image Classification CIFAR-100, 4000 Labels UPS (CNN-13) Accuracy 59.23 #1 of 1 Archive leaderboard report
Semi-Supervised Image Classification cifar-100, 10000 Labels UPS (CNN-13) Percentage error 32 #24 of 29 Archive leaderboard report
Semi-supervised Medical Image Classification Chest X-Ray14 2% labeled UPS AUC 65.51 #3 of 4 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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