Dynamic Depth Learning in Stacked AutoEncoders

Abstract

The effectiveness of deep learning models depends on their architecture and topology. Thus, it is essential to determine the optimal depth of the network. In this paper, we propose a novel approach to learn the optimal depth of a stacked AutoEncoder, called Dynamic Depth for Stacked AutoEncoders (DDSAE). DDSAE learns in an unsupervised manner the depth of a stacked AutoEncoder while training the network model. Specifically, we propose a novel objective function, aside from the AutoEncoder’s loss function to optimize the network depth: The optimization of the objective function determines the layers’ relevance weights. Additionally, we propose an algorithm that iteratively prunes the irrelevant layers based on the learned relevance weights. The performance of DDSAE was assessed using benchmark and real datasets.