# The Reversible Residual Network: Backpropagation Without Storing Activations Source: [The Reversible Residual Network: Backpropagation Without Storing Activations]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:43:17.087Z Authors: Aidan N. Gomez, Mengye Ren, Raquel Urtasun, Roger B. Grosse Published: 2017-07-14T03:05:43.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Classification on CIFAR-10 (test) (Accuracy)](): Image Classification; CIFAR-10 (test); Accuracy: 94.24. - [Image Classification on CIFAR-100 (test)](): Image Classification; CIFAR-100 (test); Accuracy: not recorded. ## Original sources and implementation [Original paper]() [PDF]()