Entropy Minimization

Entropy minimization (EM) is a technique used to improve the robustness and accuracy of machine learning models, particularly in scenarios with limited labeled data or significant distributional shifts between training and testing data. Current research focuses on refining EM methods by integrating them with other techniques like gradient norm regularization, ensemble methods, and cosine alignment to address issues such as noisy gradients and uncalibrated probabilities, leading to improved performance in various applications including image classification, speech recognition, and medical image analysis. The development of more effective EM-based approaches holds significant promise for enhancing the reliability and generalizability of machine learning models across diverse domains and datasets.