Pseudo-Label Reliability Collapse: Identification, Decomposition, and Calibration for Continual Semi-Supervised Learning

Continual semi-supervised learning (CSSL) faces a critical yet underexplored failure mode beyond catastrophic forgetting, pseudo-label reliability collapse, wherein erroneous pseudo-labels generated under irreversible data access constraints progressively corrupt feature representations across the task stream, precluding the iterative self-correction that underpins standard semi-supervised learning. Existing approaches from both communities are structurally insufficient to address this collapse, as confidence-based pseudo-label selection cannot detect over-confident but semantically inconsistent predictions. To this end, we propose a Reliability-aware Calibration Framework (RCF) grounded in a principled decomposition of pseudo-label reliability into predictive and semantic dimensions. Each unlabeled sample is embedded in a joint predictive-semantic reliability space, where predictive reliability captures augmentation-consistent prediction stability and semantic reliability measures prototype-supported class agreement in the frozen feature space. To estimate the true reliability state of unlabeled samples without ground-truth supervision, we model the resulting reliability representations with a two-component Gaussian Mixture Model whose parameters are inferred via Expectation-Maximization, yielding a calibrated, distribution-relative reliability score that is robust to task-wise distribution shifts. Building on this score, we introduce an adaptive, risk-constrained pseudo-label selection strategy with an asymmetric exponential moving average update rule, which rapidly exploits genuine reliability improvements while conservatively suppressing potential noise propagation. Extensive experiments demonstrate that RCF consistently and significantly outperforms state-of-the-art CSSL methods.