Learning invariance from transformation sequences
Neural Computation
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Categorize Objects Using Temporal Coherence
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Selective Attention in the Learning of Viewpoint and Position Invariance
Attention in Cognitive Systems. Theories and Systems from an Interdisciplinary Viewpoint
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Incorporation of visual-related self-action signals can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by eye movements and covert attention shifts. Training of the network is controlled by signals associated with eye movements and covert attention shifting. A temporal perceptual stability constraint is used to drive the output of the network toward remaining constant across temporal sequences of saccadic motions and covert attention shifts. We use a four-layer neural network model to perform the position-invariant extraction of local features and temporal integration of invariant presentations of local features in a bottom-up structure. We present results on both simulated data and real images to demonstrate that our network can acquire both position and attention shift invariance.