Learning Paired-associate Images with An Unsupervised Deep Learning Architecture
This work addresses associative memory simulation for cognitive modeling, but it is incremental as it builds on existing unsupervised and multi-modal learning techniques.
The paper tackles the problem of learning associative representations between paired images using an unsupervised deep learning architecture, achieving accuracy comparable to back-propagation networks on MNIST digit pairs.
This paper presents an unsupervised multi-modal learning system that learns associative representation from two input modalities, or channels, such that input on one channel will correctly generate the associated response at the other and vice versa. In this way, the system develops a kind of supervised classification model meant to simulate aspects of human associative memory. The system uses a deep learning architecture (DLA) composed of two input/output channels formed from stacked Restricted Boltzmann Machines (RBM) and an associative memory network that combines the two channels. The DLA is trained on pairs of MNIST handwritten digit images to develop hierarchical features and associative representations that are able to reconstruct one image given its paired-associate. Experiments show that the multi-modal learning system generates models that are as accurate as back-propagation networks but with the advantage of a bi-directional network and unsupervised learning from either paired or non-paired training examples.