Softmax Loss

Softmax loss is a fundamental component of many deep learning models, primarily used for multi-class classification by assigning probabilities to different classes. Current research focuses on improving softmax loss's performance and robustness through modifications like incorporating margins, adaptive distillation, and alternative loss functions that address issues such as noisy data, imbalanced classes, and the need for efficient model compression. These advancements lead to improved accuracy, fairness, and robustness in various applications, including face recognition, recommendation systems, and visual recognition tasks, impacting both the theoretical understanding and practical deployment of deep learning models.