Categorical Cross Entropy

Categorical cross-entropy (CCE) is a widely used loss function for multi-class classification problems, but recent research highlights its limitations, particularly in scenarios with ordinal relationships between classes or few-shot learning settings. Current research focuses on improving CCE's performance by incorporating distance metric learning (DML) techniques, developing alternative loss functions that explicitly account for class order or distance, and exploring novel architectures that promote unimodal output distributions. These advancements aim to enhance the accuracy and reliability of classification models across diverse applications, including natural language processing, medical image analysis, and legal text processing.