Learning Invariances Via Spatio-Temporal Constraints
A model for unsupervised learning of visual invariances is presented. The learning method involves a linear combination of anti-Hebbian and Hebbian weight changes, over short and long time scales, respectively. The model is demonstrated on the problem of estimating sub-pixel stereo disparity from a temporal sequence of unprocessed image pairs. After learning on a given image sequence, the model’s ability to detect sub-pixel disparity generalises, without additional learning, to image pairs from other sequences.