ConvART: Improving Adaptive Resonance Theory for Unsupervised Image Clustering
Ilia Sucholutsky, Matthias Schonlau · Journal of Computational Vision and Imaging Systems · 2018
While supervised learning techniques have become increasinglyadept at separating images into different classes, these techniquesrequire large amounts of labelled data which may not always beavailable. We propose a novel neuro-dynamic method for unsuper-vised image clustering by combining 2 biologically-motivated mod-els: Adaptive Resonance Theory (ART) and Convolutional Neu-ral Networks (CNN). ART networks are unsupervised clustering al-gorithms that have high stability in preserving learned informationwhile quickly learning new information. Meanwhile, a major prop-erty of CNNs is their translation and distortion invariance, whichhas led to their success in the domain of vision problems. Byembedding convolutional layers into an ART network, the usefulproperties of both networks can be leveraged to identify differentclusters within unlabelled image datasets and classify images intothese clusters. In exploratory experiments, we demonstrate thatthis method greatly increases the performance of unsupervisedART networks on a benchmark image dataset.