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The Reversible Residual Network: Backpropagation Without Storing Activations



Residual Networks (ResNets) have demonstrated significant improvement over traditional Convolutional Neural Networks (CNNs) on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck as one needs to store all the intermediate activations for calculating gradients using backpropagation. In this work, we present the Reversible Residual Network (RevNet), a variant of ResNets where each layer’s activations can be reconstructed exactly from the next layer’s. Therefore, the activations for most layers need not be stored in memory during backprop. We demonstrate the effectiveness of RevNets on CIFAR and ImageNet, establishing nearly identical performance to equally-sized ResNets, with activation storage requirements independent of depth.


Aidan N Gomez, Mengye Ren, Raquel Urtasun, Roger Grosse


NeurIPS 2017

Full Paper

‘The Reversible Residual Network: Backpropagation Without Storing Activations’ (PDF)

Uber ATG

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Mengye Ren is a research scientist at Uber ATG Toronto. He is also a PhD student in the machine learning group of the Department of Computer Science at the University of Toronto. He studied Engineering Science in his undergrad at the University of Toronto. His research interests are machine learning, neural networks, and computer vision. He is originally from Shanghai, China.
Raquel Urtasun is the Chief Scientist for Uber ATG and the Head of Uber ATG Toronto. She is also a Professor at the University of Toronto, a Canada Research Chair in Machine Learning and Computer Vision and a co-founder of the Vector Institute for AI. She is a recipient of an NSERC EWR Steacie Award, an NVIDIA Pioneers of AI Award, a Ministry of Education and Innovation Early Researcher Award, three Google Faculty Research Awards, an Amazon Faculty Research Award, a Connaught New Researcher Award, a Fallona Family Research Award and two Best Paper Runner up Prize awarded CVPR in 2013 and 2017. She was also named Chatelaine 2018 Woman of the year, and 2018 Toronto’s top influencers by Adweek magazine