Strengthen Out-of-Distribution Detection Capability with Progressive Self-Knowledge Distillation

Out-of-distribution (OOD) detection aims to ensure AI system reliability by rejecting inputs outside the training distribution. Recent work shows that memorizing atypical samples during later stages of training can hurt OOD detection, while strategies for forgetting them show promising improvements. However, directly forgetting atypical samples sacrifices ID generalization and limits the model's OOD detection capability. To address this issue, we propose Progressive Self-Knowledge Distillation (PSKD) framework, which strengthens the OOD detection capability by leveraging self-provided uncertaintyembedded targets. Specifically, PSKD adaptively selects a self-teacher model from the training history using pseudo-outliers, facilitating the learning of uncertainty knowledge via multi-level distillation applied to features and responses. As a result, PSKD achieves better ID generalization and uncertainty estimation for OOD detection. Moreover, PSKD is orthogonal to most existing methods and can be integrated as a plugin to collaborate with them. Experimental results from multiple OOD scenarios verify the effectiveness and general applicability of PSKD.