Learning Customized Activation: A Novel Approach to Boost Deep Neural Network Performance

This paper investigates how to enhance the performance of deep neural networks through adaptive activation functions. The authors introduce a method called Learning Customized Activation (LCA) that automatically learns the activation function best suited for a specific task. The paper describes the design and implementation of LCA and conducts experiments on multiple datasets, demonstrating that LCA significantly improves model performance. The authors also explore LCA's interpretability and visualization methods. Finally, they propose future research directions.