The Reversible Residual Network: Backpropagation Without Storing Activations

Aidan Gomez^*, Mengye Ren^*, Raquel Urtasun, Roger B. Grosse

Department of Computer Science, University of Toronto, Toronto ON, CANADA
^*Equal contribution

Abstract

Deep residual networks (ResNets) have significantly pushed forward the state-of-the-art on image classification, increasing in performance as networks grow both deeper and wider. However, memory consumption becomes a bottleneck, as one needs to store the activations in order to calculate gradients using backpropagation. 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 backpropagation. We demonstrate the effectiveness of RevNets on CIFAR-10, CIFAR-100, and ImageNet, establishing nearly identical classification accuracy to equally-sized ResNets, even though the activation storage requirements are independent of depth.

Full Paper

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Code

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Cite

@inproceedings{gomez17revnet,
  author    = {Aidan N. Gomez and 
               Mengye Ren and 
               Raquel Urtasun and 
               Roger B. Grosse},
  title     = {The Reversible Residual Network: Backpropagation without 
               Storing Activations}
  booktitle = {NIPS},
  year      = {2017},
}