Learning Viewpoint Invariant Face Representations from Visual Experience in an Attractor Network

Abstract

In natural visual experience, different views of an object or face tend to appear in close temporal proximity as an animal manipulates the object or navigates around it, or as a face changes expression or pose. A set of simulations is presented which demonstrate how viewpoint invariant representations of faces can be developed from visual experience by capturing the temporal relationships among the input patterns. The simulations explored the interaction of temporal smoothing of activity signals with Hebbian learning Foldiak (1991) in both a feedforward layer and a second, recurrent layer of a network. The feedforward connections were trained by Competitive Hebbian Learning with temporal smoothing of the post-synaptic unit activities (Bartlett & Sejnowski, 1996). The recurrent layer was a generalization of a Hopfield network with a lowpass temporal filter on all unit activities. The combination of basic Hebbian learning with temporal smoothing of unit activities produced an attractor network learning rule that associated temporally proximal input patterns into basins of attraction. These two mechanisms were demonstrated in a model that took graylevel images of faces as input. Following training on image sequences of faces as they changed pose, multiple views of a given face fell into the same basin of attraction, and the system acquired representations of faces that were approximately viewpoint invariant.