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Convolutional neural networks are witnessing wide adoption in computer vision systems with numerous applications across a range of visual recognition tasks. Much of this progress is fueled through advances in convolutional neural network architectures and learning algorithms even as the basic premise of a convolutional layer has remained unchanged. In this paper, we seek to revisit the convolutional layer that has been the workhorse of state-of-the-art visual recognition models. We introduce adoi:10.1109/cvpr.2018.00349 dblp:conf/cvpr/Juefei-XuBS18 fatcat:4r35f3ppprb4phabfgfjhcj2ta