An unsupervised deep-learning architecture that can reconstruct paired images

Abstract

This paper presents an unsupervised learning system that develops an associative memory structure that combines two or more channels of input/output such that input on one channel will correctly generate the associated response at the other channel and vice versa. A deep learning architecture is described that can reconstruct an image of a MNIST handwritten digit from another paired handwritten digit image. In this way, the system develops a kind of supervised classification model meant to simulate aspects of human associative memory. The system uses stacked layers of unsupervised Restricted Boltzmann Machines connected by a hybrid associative-supervised top layer to ensure the development of a set of high-level features that can reconstruct one image given another in either direction. Experimentation shows that the system reconstructs accurate matching paired-images that compares favourably to a back-propagation network solution. © 2013 Springer-Verlag.