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• W2947556543 abstract "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, andpropose network deconvolution, a procedure which optimally removes pixel-wiseand channel-wise correlations before the data is fed into each layer. Networkdeconvolution can be efficiently calculated at a fraction of the computationalcost of a convolution layer. We also show that the deconvolution filters in thefirst layer of the network resemble the center-surround structure found inbiological neurons in the visual regions of the brain. Filtering with suchkernels results in a sparse representation, a desired property that has beenmissing in the training of neural networks. Learning from the sparserepresentation promotes faster convergence and superior results without the useof batch normalization. We apply our network deconvolution operation to 10modern neural network models by replacing batch normalization within each.Extensive experiments show that the network deconvolution operation is able todeliver performance improvement in all cases on the CIFAR-10, CIFAR-100, MNIST,Fashion-MNIST, Cityscapes, and ImageNet datasets." @default.
• W2947556543 created "2019-06-07" @default.
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• W2947556543 date "2019-05-28" @default.
• W2947556543 modified "2023-09-22" @default.
• W2947556543 title "Network Deconvolution" @default.
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