Margin Softmax

Margin softmax loss functions aim to improve the discriminative power of deep learning models by increasing the separation between different classes in the feature space. Current research focuses on refining these losses, addressing issues like class imbalance and computational cost through techniques such as adaptive margin scaling, partial updates of fully connected layers, and integration with other loss functions like focal loss and optimal transport. These advancements lead to improved performance in various applications, including image retrieval, face recognition, and speaker verification, by enhancing feature representation learning and generalization capabilities.