Softmax Cross Entropy

Softmax cross-entropy is a widely used loss function in machine learning, primarily for multi-class classification problems, aiming to maximize the probability of the correct class. Current research focuses on improving its robustness and efficiency in challenging scenarios, such as imbalanced datasets, class-incremental learning, and federated learning settings. This involves developing modified loss functions, like adaptive margin and cyclical focal loss variations, and incorporating techniques such as logits calibration to address issues of overfitting and skewed label distributions. These advancements enhance the accuracy and reliability of classification models across diverse applications, including image recognition, natural language processing, and medical image analysis.