论文标题
关于激活功能及其与Xavier的关系的调查，他正常初始化
A Survey on Activation Functions and their relation with Xavier and He Normal Initialization
论文作者
论文摘要
在人工神经网络中，激活函数和权重初始化方法在神经网络的训练和性能中起着重要作用。出现的问题是，函数的属性对于表现出色的激活函数至关重要/必要。同样，使用最广泛的权重初始化方法 - Xavier和HE正常初始化与激活函数具有基本联系。该调查讨论了激活函数的重要/必要特性以及最广泛使用的激活功能（Sigmoid，Tanh，Relu，Lrelu和Prelu）。该调查还探讨了这些激活函数与两个权重初始化方法之间的关系 - Xavier和他正常的初始化。
In artificial neural network, the activation function and the weight initialization method play important roles in training and performance of a neural network. The question arises is what properties of a function are important/necessary for being a well-performing activation function. Also, the most widely used weight initialization methods - Xavier and He normal initialization have fundamental connection with activation function. This survey discusses the important/necessary properties of activation function and the most widely used activation functions (sigmoid, tanh, ReLU, LReLU and PReLU). This survey also explores the relationship between these activation functions and the two weight initialization methods - Xavier and He normal initialization.
