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@article{retsinas_recnets_2019, title = {{RecNets}: Channel-wise Recurrent Convolutional Neural Networks}, url = {http://arxiv.org/abs/1905.11910}, shorttitle = {{RecNets}}, abstract = {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.}, journaltitle = {{arXiv}:1905.11910 [cs, stat]}, author = {Retsinas, George and Elafrou, Athena and Goumas, Georgios and Maragos, Petros}, urldate = {2020-01-28}, date = {2019-05-28}, eprinttype = {arxiv}, eprint = {1905.11910}, keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}, }