Highway Network Block with Gates Constraints for Training Very Deep Networks

Abstract

In this paper, we propose to reformulate the learning of the highway network block to realize both early optimization and improved generalization of very deep networks while preserving the network depth. Gate constraints are duly employed to improve optimization, latent representations and parameterization usage in order to efficiently learn hierarchical feature transformations which are crucial for the success of any deep network. One of the earliest very deep models with over 30 layers that was successfully trained relied on highway network blocks. Although, highway blocks suffice for alleviating optimization problem via improved information flow, we show for the first time that further in training such highway blocks may result into learning mostly untransformed features and therefore a reduction in the effective depth of the model; this could negatively impact model generalization performance. Using the proposed approach, 15-layer and 20-layer models are successfully trained with one gate and a 32-layer model using three gates. This leads to a drastic reduction of model parameters as compared to the original highway network. Extensive experiments on CIFAR-10, CIFAR-100, Fashion-MNIST and USPS datasets are performed to validate the effectiveness of the proposed approach. Particularly, we outperform the original highway network and many state-of-the-art results. To the best our knowledge, on the Fashion-MNIST and USPS datasets, the achieved results are the best reported in literature.