Entropy Sampling

Entropy sampling is a technique used to select data points based on their information content, aiming to maximize the information gained from limited samples. Current research focuses on applying entropy sampling in diverse areas, including active learning for medical image segmentation, improving the robustness and accuracy of deep neural networks, and optimizing data acquisition in applications like magnetic resonance imaging. These applications leverage entropy-based strategies alongside techniques like diffusion models and UMAP for dimensionality reduction, demonstrating its value in improving efficiency and performance across various machine learning tasks. The resulting improvements in data efficiency and model performance have significant implications for resource-constrained applications and the development of more robust and accurate AI systems.