ReLU Neural Networks and Their Training

Abstract

Among various activation functions, the Rectified Linear Unit (ReLU) has become the most widely adopted due to its computational simplicity and effectiveness in mitigating the vanishing-gradient problem. In this work, we investigate the advantages of employing ReLU as the activation function and establish its theoretical significance. Our analysis demonstrates that ReLU-based neural networks possess the universal approximation property. In addition, we provide a theoretical explanation for the phenomenon of neuron death in ReLU-based neural networks. We further validate the effectiveness of this explanation through empirical experiments.