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Network Deconvolution
[article]
2020
arXiv
pre-print
Convolution is a central operation in Convolutional Neural Networks (CNNs), which applies a kernel to overlapping regions shifted across the image. However, because of the strong correlations in real-world image data, convolutional kernels are in effect re-learning redundant data. In this work, we show that this redundancy has made neural network training challenging, and propose network deconvolution, a procedure which optimally removes pixel-wise and channel-wise correlations before the data
arXiv:1905.11926v4
fatcat:r2xsc6f2bbfsxe5l2scenl2zze