Meng Wang

Boosting Semi-Supervised Learning With Entropy-Guided Adaptive Reward Maximization

Existing semi-supervised learning (SSL) methods rely predominantly on pseudo-labeling and consistency regularization to leverage unlabeled data, demonstrating significant performance improvements. However, we pinpoint that these methods suffer from a confidence-for-weighting issue, overvaluing high-confidence pseudo-la...