Learning to Categorize Objects Using Temporal Coherence
Suzanna Becker · 1993
The invariance of an objects' identity as it transformed over time provides a powerful cue for perceptual learning. We present an unsupervised learning procedure which maximizes the mutual information between the representations adopted by a feed-forward network at consecutive time steps. We demonstrate that the network can learn, entirely unsupervised, to classify an ensemble of several patterns by observing pattern trajectories, even though there are abrupt transitions from one object to another between trajectories. The same learning procedure should be widely applicable to a variety of perceptual learning tasks. 1 INTRODUCTION A promising approach to understanding human perception is to try to model its developmental stages. There is ample evidence that much of perception is learned. Even some very low level perceptual abilities such as stereopsis (Held, Birch and Gwiazda, 1980; Birch, Gwiazda and Held, 1982) are not present at birth, and appear to be learned. Once rudim...