Robust Semi-Supervised Learning via Divergence-Based Empirical Risk Functions and Regularizers

This paper proposes novel empirical risk functions and regularizers inspired by f-divergences and α-Rényi divergence for self-training methods in semi-supervised learning, such as pseudo-labeling and entropy minimization. These divergence-based approaches are more robust to noisy pseudo-labels compared to traditional self-training methods.