Margin Loss

Margin loss, a class of loss functions used in machine learning, aims to improve model performance by maximizing the separation between different classes in the feature space. Current research focuses on refining margin loss functions for various applications, including object tracking, recommender systems, and open-set recognition, often incorporating techniques like adaptive margins, multiple margins, and prototype-based learning within deep learning architectures. These advancements enhance model robustness, particularly in handling imbalanced datasets, noisy labels, and unseen classes, leading to improved accuracy and efficiency in diverse fields like computer vision and medical diagnosis.