Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Proceedings of the IEEE international conference on computer …, 2015•openaccess.thecvf.com
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
propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified
unit. PReLU improves model fitting with nearly zero extra computational cost and little
overfitting risk. Second, we derive a robust initialization method that particularly considers
the rectifier nonlinearities. This method enables us to train extremely deep rectified models …
work, we study rectifier neural networks for image classification from two aspects. First, we
propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified
unit. PReLU improves model fitting with nearly zero extra computational cost and little
overfitting risk. Second, we derive a robust initialization method that particularly considers
the rectifier nonlinearities. This method enables us to train extremely deep rectified models …
Abstract
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 propose a Parametric Rectified Linear Unit (PReLU) that generalizes the traditional rectified unit. PReLU improves model fitting with nearly zero extra computational cost and little overfitting risk. Second, we derive a robust initialization method that particularly considers the rectifier nonlinearities. This method enables us to train extremely deep rectified models directly from scratch and to investigate deeper or wider network architectures. Based on the learnable activation and advanced initialization, we achieve 4.94% top-5 test error on the ImageNet 2012 classification dataset. This is a 26% relative improvement over the ILSVRC 2014 winner (GoogLeNet, 6.66%). To our knowledge, our result is the first to surpass the reported human-level performance (5.1%) on this dataset.
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