Cautious Self-Learning via Labelwise Uncertainty Quantification

Self-learning is a widely used semi-supervised learning strategy that leverages model predictions to label unlabeled data. However, conventional approaches often ignore the uncertainty inherent in these predictions, leading to confirmation bias and overconfident models. In this work, we propose a cautious self-learning framework that integrates label-wise uncertainty quantification through credal sets. By distinguishing between aleatoric and epistemic uncertainty, our method adjusts pseudo-labels to reflect both the inherent ambiguity of the data and the model’s limited knowledge. We employ a variance-based decomposition and a label-wise Beta distribution estimation to construct credal sets that mitigate class imbalance and reduce overconfidence during iterative training. Experiments across six benchmark datasets demonstrate that our approach consistently improves classification accuracy compared to hard and soft pseudo-labeling strategies, achieving superior performance. These results highlight the importance of uncertainty-aware self-learning and show that cautious pseudo-labeling can enhance robustness and generalization in semi-supervised leanrning.