An abstract deep network for image classification

Authors:
Anthony Knittel;Alan D. Blair
Affiliations:
University of New South Wales, Australia;University of New South Wales, Australia
Venue:
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Year:
2012

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Abstract

In order to allow more flexible and general learning, it is an advantage for artificial systems to be able to discover re-usable features that capture structure in the environment, known as Deep Learning. Techniques have been shown based on convolutional neural networks and stacked Restricted Boltzmann Machines, which are related to some degree with neural processes. An alternative approach using abstract representations, the ARCS Learning Classifier System, has been shown to build feature hierarchies based on reinforcement, providing a different perspective, however with limited classification performance compared to Artificial Neural Network systems. An Abstract Deep Network is presented that is based on ARCS for building the feature network, and introduces gradient descent to allow improved results on an image classification task. A number of implementations are examined, comparing the use of back-propagation at various depths of the system. The ADN system is able to produce classification error of 1.18% on the MNIST dataset, comparable with the most established general learning systems on this task. The system shows strong reliability in constructing features, and the abstract representation provides a good platform for studying further effects such as as top-down influences.