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PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning

2018-04-17ICML 2018Code Available1· sign in to hype

Yunbo Wang, Zhifeng Gao, Mingsheng Long, Jian-Min Wang, Philip S. Yu

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Abstract

We present PredRNN++, an improved recurrent network for video predictive learning. In pursuit of a greater spatiotemporal modeling capability, our approach increases the transition depth between adjacent states by leveraging a novel recurrent unit, which is named Causal LSTM for re-organizing the spatial and temporal memories in a cascaded mechanism. However, there is still a dilemma in video predictive learning: increasingly deep-in-time models have been designed for capturing complex variations, while introducing more difficulties in the gradient back-propagation. To alleviate this undesirable effect, we propose a Gradient Highway architecture, which provides alternative shorter routes for gradient flows from outputs back to long-range inputs. This architecture works seamlessly with causal LSTMs, enabling PredRNN++ to capture short-term and long-term dependencies adaptively. We assess our model on both synthetic and real video datasets, showing its ability to ease the vanishing gradient problem and yield state-of-the-art prediction results even in a difficult objects occlusion scenario.

Tasks

Video Prediction

Benchmark Results

DatasetModelMetricClaimedVerifiedStatus
KTHPredRNN++SSIM0.87—Unverified
Moving MNISTCausal LSTMMSE46.5—Unverified
SynpickVPPredRNN++LPIPS0.05—Unverified

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