Multilevel Data Representation for Training Deep Helmholtz Machines.

Abstract

A vast majority of the current research in the field of machine learning is done using algorithms with strong arguments pointing to their biological implausibility such as backpropagation, deviating the field's focus from understanding its original organic inspiration to a compulsive search for optimal performance. Yet there have been a few proposed models that respect most of the biological constraints present in the human brain and are valid candidates for mimicking some of its properties and mechanisms. In this letter, we focus on guiding the learning of a biologically plausible generative model called the Helmholtz machine in complex search spaces using a heuristic based on the human image perception mechanism. We hypothesize that this model's learning algorithm is not fit for deep networks due to its Hebbian-like local update rule, rendering it incapable of taking full advantage of the compositional properties that multilayer networks provide. We propose to overcome this problem by providing the network's hidden layers with visual queues at different resolutions using multilevel data representation. The results on several image data sets showed that the model was able to not only obtain better overall quality but also a wider diversity in the generated images, corroborating our intuition that using our proposed heuristic allows the model to take more advantage of the network's depth growth. More important, they show the unexplored possibilities underlying brain-inspired models and techniques.