Learning of Position-Invariant Object Representation Across Attention Shifts

Selective attention shift can help neural networks learn invariance. We describe a method that can produce a network with invariance to changes in visual input caused by attention shifts. Training of the network is controlled by signals associated with attention shifting. A temporal perceptual stabi...

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Bibliographische Detailangaben

Hauptverfasser:	Li, Muhua, Clark, James J.
Format:	Buchkapitel
Sprache:	eng
Schlagworte:	Applied sciences Artificial intelligence Computer science control theory systems Exact sciences and technology Hide Layer Learning Rule Local Feature Neural Response Pattern recognition. Digital image processing. Computational geometry Retinal Image
Online-Zugang:	Volltext
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