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We explore the redundancy of parameters in deep neural networks by replacing the conventional linear projection in fully-connected layers with the circulant projection.
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Exploiting the circulant structure of tracking-by-detection with kernels
J. Henriques, R. Caseiro, P. Martins, and J. Batista · 2012
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Improving neural networks by preventing coadaptation of feature detectors
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Beyond hard negative mining: Efficient detector learning via block-circulant decomposition
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Overfeat: Integrated recognition, localization and detection using convolutional networks
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Deep learning face representation from predicting 10,000 classes
Y. Sun, X. Wang, and X. Tang · 2014
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Circulant binary embedding
F. X. Yu, S. Kumar, Y. Gong, and S.-F. Chang · 2014
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Visualizing and understanding convolutional networks
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Deeply learned face representations are sparse, selective, and robust
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Compact nonlinear maps and circulant extensions
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