  In this paper, we introduce Channel-wise recurrent convolutional neural
networks (RecNets), a family of novel, compact neural network architectures for
computer vision tasks inspired by recurrent neural networks (RNNs). RecNets
build upon Channel-wise recurrent convolutional (CRC) layers, a novel type of
convolutional layer that splits the input channels into disjoint segments and
processes them in a recurrent fashion. In this way, we simulate wide, yet
compact models, since the number of parameters is vastly reduced via the
parameter sharing of the RNN formulation. Experimental results on the CIFAR-10
and CIFAR-100 image classification tasks demonstrate the superior size-accuracy
trade-off of RecNets compared to other compact state-of-the-art architectures.
