Selective Attention Improves Learning

被引：0

作者：

Yli-Krekola, Antti ^{[1
]}

Sarela, Jaakko ^{[1
]}

Valpola, Harri ^{[1
]}

机构：

[1] Aalto Univ, Dept Biomed Engn & Computat Sci, Helsinki, Finland

来源：

ARTIFICIAL NEURAL NETWORKS - ICANN 2009, PT II | 2009年 / 5769卷

关键词：

Selective attention; perceptual learning; segmentation; VISUAL-ATTENTION; SEGMENTATION; ALGORITHM; MECHANISM; SEARCH; MODEL;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

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.

引用

页码：285 / 294

页数：10

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