@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]},
	eprinttype = {jstor},
	eprint = {http://arxiv.org/abs/1905.11910},
	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}
}