Handling Vanishing Gradient Problem Using Artificial Derivative
Zheng Hu, Jiaojiao Zhang, Yun Ge
Abstract
Sigmoid function and ReLU are commonly used activation functions in neural networks (NN). However, sigmoid function is vulnerable to the vanishing gradient problem, while ReLU has a special vanishing gradient problem that is called dying ReLU problem. Though many studies provided methods to alleviate this problem, there has not been an efficient feasible solution. Hence, we proposed a method replacing the original derivative function with an artificial derivative in a pertinent way. Our method optimized gradients of activation functions without varying activation functions nor introducing extra layers. Our investigations demonstrated that the method can effectively alleviate the vanishing gradient problem for both ReLU and sigmoid function with few computational cost.