Demystifying sparse rectified auto-encoders

Abstract

Auto-Encoders can learn features similar to Sparse Coding, but the training can be done efficiently via the back-propagation algorithm as well as the features can be computed quickly for a new input. However, in practice, it is not easy to get Sparse Auto-Encoders working; there are two things that need investigating: sparsity constraint and weight constraint. In this paper, we try to understand the problem of training Sparse Auto-Encoders with L1-norm sparsity penalty, and propose a modified version of Stochastic Gradient Descent algorithm, called Sleep-Wake Stochastic Gradient Descent (SW-SGD), to solve this problem. Here, we focus on Sparse Auto-Encoders with rectified linear units in the hidden layer, called Sparse Rectified Auto-Encoders (SRAEs), because such units compute fast and can produce true sparsity (exact zeros). In addition, we propose a new reasonable way to constrain SRAEs' weights. Experiments on MNIST dataset show that the proposed weight constraint and SW-SGD help SRAEs successfully learn meaningful features that give excellent performance on classification task compared to other Auto-Encoder variants.