A New Training Principle for Stacked Denoising Autoencoders

Abstract

In this work, a new training principle is introduced for unsupervised learning that makes the learned representations more efficient and useful. Using partially corrupted inputs instead, the denoising Auto encoder can obtain more robust and representative pattern of inputs than the traditional learning methods. Besides, this denoising Auto encoder can be stacked to form a deep network. The whole framework of training stacked denoising Auto encoders, which involved several supervised training methods in the framework, is given for image classification. Comparative experiments have shown that the model can resist noise of training examples powerfully and achieve better accuracy of image classification on MNIST database.