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arXiv:1505.00393cs.CV

ReNet: A Recurrent Neural Network Based Alternative to Convolutional Networks

Francesco Visin, Kyle Kastner, Kyunghyun Cho, Matteo Matteucci, Aaron Courville, Yoshua Bengio

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Abstract

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.

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cs.CVComputer Vision and Pattern Recognition

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Linked by 1 identifiable commenter in 1 comment across 1 thread, from Aug 2015 to Aug 2015. Found in 1 HN submission from May 2015 to May 2015. Open submission discussions.

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  1. Classical music generation with recurrent neural networks 1 comment

    • This is really cool stuff - the network structure reminds me a lot of Graves' MDRNN[1] and Grid LSTM[2], as well as some work I helped with (ReNet [3]) I wonder if the structure over frequency/time is too "regular" - in general for sound the frequency correlation and the time…

      kastnerkyleView on HNParent context

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