  In this paper, we propose a deep neural network architecture for object
recognition based on recurrent neural networks. The proposed network, called
ReNet, replaces the ubiquitous convolution+pooling layer of the deep
convolutional neural network with four recurrent neural networks that sweep
horizontally and vertically in both directions across the image. We evaluate
the proposed ReNet on three widely-used benchmark datasets; MNIST, CIFAR-10 and
SVHN. The result suggests that ReNet is a viable alternative to the deep
convolutional neural network, and that further investigation is needed.
