Clayton Mellina

CReST: A Class-Rebalancing Self-Training Framework for Imbalanced Semi-Supervised Learning

Semi-supervised learning on class-imbalanced data, although a realistic problem, has been under studied. While existing semi-supervised learning (SSL) methods are known to perform poorly on minority classes, we find that they still generate high precision pseudo-labels on minority classes. By exploi…