EraseReLU: A Simple Way to Ease the Training of Deep Convolution Neural Networks

Abstract

For most state-of-the-art architectures, Rectified Linear Unit (ReLU) becomes a standard component accompanied with each layer. Although ReLU can ease the network training to an extent, the character of blocking negative values may suppress the propagation of useful information and leads to the difficulty of optimizing very deep Convolutional Neural Networks (CNNs). Moreover, stacking layers with nonlinear activations is hard to approximate the intrinsic linear transformations between feature representations. In this paper, we investigate the effect of erasing ReLUs of certain layers and apply it to various representative architectures following deterministic rules. It can ease the optimization and improve the generalization performance for very deep CNN models. We find two key factors being essential to the performance improvement: 1) the location where ReLU should be erased inside the basic module; 2) the proportion of basic modules to erase ReLU; We show that erasing the last ReLU layer of all basic modules in a network usually yields improved performance. In experiments, our approach successfully improves the performance of various representative architectures, and we report the improved results on SVHN, CIFAR-10/100, and ImageNet. Moreover, we achieve competitive single-model performance on CIFAR-100 with 16.53% error rate compared to state-of-the-art.

References in corpus (15)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Deep Residual Learning for Image Recognition
• Empirical Evaluation of Rectified Activations in Convolutional Network
• Wide Residual Networks
• Going Deeper with Convolutions
• Rethinking the Inception Architecture for Computer Vision
• Rich feature hierarchies for accurate object detection and semantic segmentation
• Aggregated Residual Transformations for Deep Neural Networks
• Shake-Shake regularization
• Convolutional Pose Machines
• Deep Networks with Stochastic Depth
• DiracNets: Training Very Deep Neural Networks Without Skip-Connections
• Detecting and Recognizing Human-Object Interactions
• More is Less: A More Complicated Network with Less Inference Complexity
• Rethink ReLU to Training Better CNNs

Cited by in corpus (2)

• Learning to play the Chess Variant Crazyhouse above World Champion Level with Deep Neural Networks and Human Data
• Parametric Variational Linear Units (PVLUs) in Deep Convolutional Networks