Selective Attention Improves Learning

Authors:
Antti Yli-Krekola;Jaakko Särelä;Harri Valpola
Affiliations:
Department of Biomedical Engineering and Computational Science, Aalto University, Helsinki, Finland;Department of Biomedical Engineering and Computational Science, Aalto University, Helsinki, Finland;Department of Biomedical Engineering and Computational Science, Aalto University, Helsinki, Finland
Venue:
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
Year:
2009

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Abstract

We demonstrate that selective attention can improve learning. Considerably fewer samples are needed to learn a source separation problem when the inputs are pre-segmented by the proposed model. The model combines biased-competition model for attention with a habituation mechanism which allows the focus of attention to switch from one object to another. The criteria for segmenting objects are estimated from data and are shown to generalise to new objects.