Normalization techniques are essential for accelerating the training and improving the
generalization of deep neural networks (DNNs), and have successfully been used in various …

In recent years, deep artificial neural networks (including recurrent ones) have won
numerous contests in pattern recognition and machine learning. This historical survey …

鉴于深度学习的重要性, 综述了深度学习的研究进展. 首先概述了深度学习具有的优点,
由此说明了引入深度学习的必要性; 然后描述了三种典型的深度学习模型 …

In this letter, we present a conditional generative adversarial network-based model for real-
time underwater image enhancement. To supervise the adversarial training, we formulate an …

We present weight normalization: a reparameterization of the weight vectors in a neural
network that decouples the length of those weight vectors from their direction. By …

Theoretical and empirical evidence indicates that the depth of neural networks is crucial for
their success. However, training becomes more difficult as depth increases, and training of …

There is plenty of theoretical and empirical evidence that depth of neural networks is a
crucial ingredient for their success. However, network training becomes more difficult with …

Abstract Training Deep Neural Networks is complicated by the fact that the distribution of
each layer's inputs changes during training, as the parameters of the previous layers …

Rectified activation units (rectifiers) are essential for state-of-the-art neural networks. In this
work, we study rectifier neural networks for image classification from two aspects. First, we …

The increasing integration of the Internet of Everything into the industrial value chain has
built the foundation for the next industrial revolution called Industrie 4.0. Although Industrie …