Angular Margin Loss

Angular margin loss functions aim to improve the discriminative power of deep learning models by increasing the angular separation between feature vectors of different classes. Current research focuses on adapting the margin dynamically, addressing class imbalance issues, and handling challenges like angular boundary discontinuities in applications such as object detection and sound classification. These advancements lead to improved performance in various tasks, including face recognition, object detection in aerial imagery, and anomalous sound detection, demonstrating the practical impact of refined angular margin loss techniques.