Learning Customized Activation Functions: Enhancing Neural Network Performance

This paper presents a novel method for learning customized activation functions, aiming to enhance the performance of neural networks. The proposed approach utilizes adaptive activation functions, where each neuron learns its own activation function, allowing it to adapt to different input data and tasks. Experiments demonstrate that this method achieves notable improvements in model accuracy and generalization ability across various datasets.