Unsupervised Segmentation with Dynamical Units

Abstract—We present a novel network to deconvolve mixtures of inputs that have been previously learned, but more importantly, to segment the components of each input object that most contribute to its classification. The network consists of amplitude-phase units that can synchronize their dynamics, so that deconvolution is determined by the amplitude of an output layer, and segmentation by phase similarity between input and output layer units. Learning is unsupervised and based on Hebbian update, and the architecture is very simple. Moreover, efficient segmentation can be achieved even when there is considerable superposition of the inputs.

By: A. Ravishankar Rao; Guillermo A. Cecchi; Charles C. Peck; James R. Kozloski

Published in: RC23838 in 2005


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