Classification Layer

Classification layers are the final components of many neural networks, responsible for assigning input data to predefined categories. Current research focuses on improving their calibration, reliability, and efficiency, particularly within architectures like Wide Residual Networks and Vision Transformers, often through techniques like decoupling feature extraction and classification training or employing contrastive learning methods. These advancements are crucial for enhancing the safety and trustworthiness of AI systems in high-stakes applications such as healthcare and autonomous driving, while also addressing challenges like inference attacks and catastrophic forgetting in federated learning scenarios.