Confidence Relaxation

Confidence relaxation, in various contexts, aims to improve model performance and robustness by mitigating the negative effects of overly confident predictions or hard constraints. Current research focuses on applying this principle to diverse areas, including improving the efficiency and accuracy of machine learning algorithms (e.g., Gromov-Wasserstein distance approximation, k-means clustering), enhancing video quality assessment and speech recognition, and developing more generalizable self-supervised learning models. These advancements have significant implications for improving the reliability and applicability of machine learning across numerous fields, from healthcare to computer vision.