Classification Head

A classification head is the final layer of a neural network responsible for assigning input data to predefined classes. Current research focuses on improving classification head performance through alternative architectures (e.g., replacing Multi-Layer Perceptrons with Fourier KAN, Graph Neural Networks, or Nadaraya-Watson heads), optimizing information flow between the feature extractor and the head (e.g., using uncertainty-aware fusion or matrix information theory), and addressing challenges like class imbalance and data heterogeneity (e.g., in long-tailed object detection and federated learning). These advancements lead to more accurate, efficient, and robust classification across diverse applications, including image recognition, natural language processing, and medical image analysis.