Class Separability

Class separability, the degree to which different classes of data are distinguishable in a feature space, is a central challenge in machine learning, aiming to optimize both intra-class compactness and inter-class distance. Current research focuses on improving separability through novel loss functions (e.g., large margin losses, distance-based losses), refined model training strategies (e.g., mixup methods, neural collapse optimization), and the use of pre-defined prototypes or anchors to guide feature learning. These advancements enhance the performance of various machine learning models, particularly in challenging scenarios like few-shot learning, imbalanced datasets, and open-set recognition, impacting fields such as image classification, medical diagnosis, and anomaly detection.