Progressive Sample-Weighted Pseudo-Label Clustering via Global–Local Confidence Modelling

Pseudo-label-based deep clustering has emerged as a compelling paradigm for unsupervised visual learning, yet two fundamental limitations impede its effectiveness: (1) pseudo-label noise, where unreliable hard assignments generated in early training accumulate and propagate errors across iterations; and (2) confidence-insensitive pseudo-label supervision, where one-hot pseudo-labels are treated with equal importance despite substantial differences in their reliability, increasing the influence of uncertain samples, particularly those near cluster boundaries. To address both issues simultaneously, we propose PSPLC—a Progressive Sample-Weighted Pseudo-Label Clustering framework with three synergistic modules. First, a MoCo-based contrastive pre-training module extracts discriminative feature representations. Second, a progressive sample-weighted pseudo-label training module (the core contribution) iterates an expectation step—which progressively expands a high-confidence sample set outward from calibrated cluster centres and assigns one-hot pseudo-labels with continuous confidence weights derived from a global–local confidence mechanism—with a maximisation step that jointly optimises a clustering consistency loss, a confidence-weighted one-hot pseudo-label loss, and an entropy balance loss. Third, a semi-supervised boosting module (PSPLC-Boost) identifies reliable samples via nearest-neighbour semantic consistency and jointly fine-tunes the feature extractor and clustering head with a FixMatch-inspired strategy. Extensive experiments on five benchmarks (STL-10, CIFAR-10, CIFAR-20, ImageNet-10, ImageNet-Dogs-15) show that PSPLC achieves state-of-the-art performance among purely unsupervised methods, and PSPLC-Boost surpasses all competitors on all five datasets across all three metrics.