Progressive Trust Calibration for Noisy Continual Learning

Learning under noisy supervision in continual environments presents a fundamental challenge that existing methods are ill-equipped to address. Label corruption and distribution shift co-evolve across tasks, so that one-shot static noise-removal rules cannot keep pace with the evolving stream. In this work, we revisit this problem through a fresh lens and argue that sample reliability should not be treated as a fixed binary attribute, but rather as a dynamic property that evolves alongside the model's growing semantic competence. Motivated by this insight, we propose Progressive Trust Calibration (PTC), a unified framework that reframes noisy-label continual learning as a process of sample trust evolution. PTC operates through three synergistic stages. First, Relational Trust Initialization anchors reliability estimation in the intrinsic geometry of a frozen pretrained representation space, measuring neighborhood label consistency and local feature compactness to produce stable, prediction-independent trust scores that are immune to early-stage classifier bias. Second, Prototype-guided Trust Refinement elevates this local assessment to a global semantic level by constructing class prototypes from verified reliable samples and identifying violations of category-level structure, capturing noisy instances that evade local detection. Third, Expert-driven Trust Evolution closes the loop by treating the noise pool not as a discard bin but as a reservoir of recoverable supervision in which a task-specific expert continuously reassesses suspicious samples as reliable evidence accumulates, progressively relabeling informative examples that were initially flagged as unreliable. Empirical results show that our approach outperforms all compared methods.