The choice of activation functions in Convolutional Neural Networks (CNNs) is very
important. Rectified Linear Unit (ReLU) has been widely-used in most CNNs. Recently, a …

Activation functions playa key role in providing remarkable performance in deep neural
networks, and the rectified linear unit (ReLU) is one of the most widely used activation …

Rectified linear unit (ReLU) is a widely used activation function in artificial neural networks, it
is considered to be an efficient active function benefit from its simplicity and nonlinearity …

Activation function unit is an extremely important part of convolution neural networks; it is the
nonlinear transformation that we do over the input data. Using hidden layer incorporating …

The activation function plays an important role in training and improving performance in
deep neural networks (dnn). The rectified linear unit (relu) function provides the necessary …

Rectified linear unit (ReLU) is a widely used activation function for deep convolutional
neural networks. However, because of the zero-hard rectification, ReLU networks lose the …

Activation functions are essential for deep learning methods to learn and perform complex
tasks such as image classification. Rectified Linear Unit (ReLU) has been widely used and …

The activation function has a critical influence on whether a convolutional neural network in
deep learning can converge or not; a proper activation function not only makes the …

Rectified linear unit (ReLU) plays an important role in today's convolutional neural networks
(CNNs). In this paper, we propose a novel activation function called Rectified Exponential …

Rectified linear activation units are important components for state-of-the-art deep
convolutional networks. In this paper, we propose a novel S-shaped rectifiedlinear activation …