Comparison of Training Methods for Deep Neural Networks
This is an incremental study that compares existing training methods for deep neural networks, primarily benefiting researchers in machine learning by providing practical recommendations and benchmark results.
The paper reviewed training methods for deep neural networks, focusing on pre-training techniques like Deep Belief Networks and Stacked Autoencoders, and conducted experiments on MNIST and a facial emotion dataset, achieving an error rate lower than the best Kaggle competition result using an optimized Stacked Autoencoder.
This report describes the difficulties of training neural networks and in particular deep neural networks. It then provides a literature review of training methods for deep neural networks, with a focus on pre-training. It focuses on Deep Belief Networks composed of Restricted Boltzmann Machines and Stacked Autoencoders and provides an outreach on further and alternative approaches. It also includes related practical recommendations from the literature on training them. In the second part, initial experiments using some of the covered methods are performed on two databases. In particular, experiments are performed on the MNIST hand-written digit dataset and on facial emotion data from a Kaggle competition. The results are discussed in the context of results reported in other research papers. An error rate lower than the best contribution to the Kaggle competition is achieved using an optimized Stacked Autoencoder.