Performance Comparison of Three Types of Autoencoder Neural Networks

Abstract

This paper presents a comparison performance on three types of autoencoders, namely, the traditional autoencoder with Restricted Boltzmann Machine (RBM), the stacked autoencoder without RBM and the stacked autoencoder with RBM. The performances are compared based on the reconstruction error for face images and using the same values for the parameters such as the number of neurons in the hidden layers, the training method, and the learning rate. The results show that the RBM stacked autoencoder gives better performance in terms of the reconstruction error compared to the other two architectures.