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Deep Networks with Internal Selective Attention through Feedback Connections
[article]
2014
arXiv
pre-print
Traditional convolutional neural networks (CNN) are stationary and feedforward. They neither change their parameters during evaluation nor use feedback from higher to lower layers. Real brains, however, do. So does our Deep Attention Selective Network (dasNet) architecture. DasNets feedback structure can dynamically alter its convolutional filter sensitivities during classification. It harnesses the power of sequential processing to improve classification performance, by allowing the network to
arXiv:1407.3068v2
fatcat:ct5xhzbhubhctc3zpuhrfcuiga