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<p>Semi-supervised learning (SSL) leverages labeled and unlabeled data for modern classification tasks. However, existing SSL approaches often underutilize moderately uncertain samples and may propagate errors from highly uncertain pseudo-labels, leading to suboptimal performance, in noisy and class-imbalanced datasets. We introduce an SSL framework with an uncertainty-weighted training mechanism that prioritizes moderately uncertain samples while deferring extremely uncertain samples via a dynamic entropy mask. Training on unlabeled data combines masked cross-entropy with a Bhattacharyya-regularized alignment term between weak and strong predictions, improving view consistency and distribution alignment. A dynamic entropy threshold (<InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\:{\epsilon\:}_{t}\)</EquationSource> </InlineEquation>) that adapts over training, filtering only extremely uncertain pseudo-labels and thereby limiting error propagation while retaining informative unlabeled data. The proposed framework is evaluated on several benchmark datasets, including CIFAR-10, SVHN and STL-10 under label-scarce and class-imbalanced protocols, achieving up to 3–5% absolute accuracy gains over strong SSL baselines (e.g., FixMatch, ReMixMatch, FreeMatch). Our results show that the proposed approach improves model generalization and robustness, particularly in scenarios involving label noise, class imbalance, and limited labeled data, while remaining comparable on clean, class-balanced settings.</p>

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Uncertainty-weighted semi-supervised learning with dynamic entropy masking and Bhattacharyya-regularized loss

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月27日

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  • Mohammed Talal Ghazal,
  • Jafar Tanha,
  • Nasrin Shahi,
  • SeyedEhsan Roshan

摘要

Semi-supervised learning (SSL) leverages labeled and unlabeled data for modern classification tasks. However, existing SSL approaches often underutilize moderately uncertain samples and may propagate errors from highly uncertain pseudo-labels, leading to suboptimal performance, in noisy and class-imbalanced datasets. We introduce an SSL framework with an uncertainty-weighted training mechanism that prioritizes moderately uncertain samples while deferring extremely uncertain samples via a dynamic entropy mask. Training on unlabeled data combines masked cross-entropy with a Bhattacharyya-regularized alignment term between weak and strong predictions, improving view consistency and distribution alignment. A dynamic entropy threshold ( \(\:{\epsilon\:}_{t}\) ) that adapts over training, filtering only extremely uncertain pseudo-labels and thereby limiting error propagation while retaining informative unlabeled data. The proposed framework is evaluated on several benchmark datasets, including CIFAR-10, SVHN and STL-10 under label-scarce and class-imbalanced protocols, achieving up to 3–5% absolute accuracy gains over strong SSL baselines (e.g., FixMatch, ReMixMatch, FreeMatch). Our results show that the proposed approach improves model generalization and robustness, particularly in scenarios involving label noise, class imbalance, and limited labeled data, while remaining comparable on clean, class-balanced settings.