Training Better CNNs Requires to Rethink ReLU

With the rapid development of Deep Convolutional Neural Networks (DCNNs), numerous works focus on designing better network architectures (i.e., AlexNet, VGG, Inception, ResNet and DenseNet etc.). Nevertheless, all these networks have the same characteristic: each convolutional layer is followed by an activation layer, a Rectified Linear Unit (ReLU) layer is the most used among them. In this work, we argue that the paired module with 1:1 convolution and ReLU ratio is not the best choice since it may result in poor generalization ability. Thus, we try to investigate the more suitable convolution and ReLU ratio for exploring the better network architectures. Specifically, inspired by Leaky ReLU, we focus on adopting the proportional module with N:M (N>M) convolution and ReLU ratio to design the better networks. From the perspective of ensemble learning, Leaky ReLU can be considered as an ensemble of networks with different convolution and ReLU ratio. We find that the proportional module with N:M (N>M) convolution and ReLU ratio can help networks acquire the better performance, through the analysis of a simple Leaky ReLU model. By utilizing the proportional module with N:M (N>M) convolution and ReLU ratio, many popular networks can form more rich representations in models, since the N:M (N>M) proportional module can utilize information more effectively. Furthermore, we apply this module in diverse DCNN models to explore whether is the N:M (N>M) convolution and ReLU ratio indeed more effective. From our experimental results, we can find that such a simple yet effective method achieves better performance in different benchmarks with various network architectures and the experimental results verify that the superiority of the proportional module. In addition, to our knowledge, it is the first time to introduced the proportional module in DCNN models. We think that our proposed method can help many researchers design the better network architectures. Introduction Nowadays, with the available of large scale image datasets eg ImageNet (Russakovsky et al. 2015)) as well as high performance computing resources eg GPU, deep Convolutional Neural Networks (CNNs) (LeCun et al. 1998) have been dominant in many computer vision applications, especially for image classification (Krizhevsky, Sutskever, and Hinton ∗Corresponding author. ([email protected]) Copyright c © 2017, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. f (y)