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arXiv · 1910.11577

CrevNet: Conditionally Reversible Video Prediction

Wei Yu·Yichao Lu·Steve Easterbrook·Sanja Fidler
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Abstract

Applying resolution-preserving blocks is a common practice to maximize information preservation in video prediction, yet their high memory consumption greatly limits their application scenarios. We propose CrevNet, a Conditionally Reversible Network that uses reversible architectures to build a bijective two-way autoencoder and its complementary recurrent predictor. Our model enjoys the theoretically guaranteed property of no information loss during the feature extraction, much lower memory consumption and computational efficiency.

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Wei Yu, Yichao Lu, Steve Easterbrook, Sanja Fidler. 2019-10-25. CrevNet: Conditionally Reversible Video Prediction. https://arxiv.org/abs/1910.11577

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