A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction

Moitreya Chatterjee; Narendra Ahuja; Anoop Cherian

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arXiv · 2110.03446

A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction

Moitreya Chatterjee·Narendra Ahuja·Anoop Cherian

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Abstract

Predicting the future frames of a video is a challenging task, in part due to the underlying stochastic real-world phenomena. Prior approaches to solve this task typically estimate a latent prior characterizing this stochasticity, however do not account for the predictive uncertainty of the (deep learning) model. Such approaches often derive the training signal from the mean-squared error (MSE) between the generated frame and the ground truth, which can lead to sub-optimal training, especially when the predictive uncertainty is high. Towards this end, we introduce Neural Uncertainty Quantifier (NUQ) - a stochastic quantification of the model's predictive uncertainty, and use it to weigh the MSE loss. We propose a hierarchical, variational framework to derive NUQ in a principled manner using a deep, Bayesian graphical model. Our experiments on four benchmark stochastic video prediction datasets show that our proposed framework trains more effectively compared to the state-of-the-art models (especially when the training sets are small), while demonstrating better video generation quality and diversity against several evaluation metrics.

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BibTeX RIS

Moitreya Chatterjee, Narendra Ahuja, Anoop Cherian. 2021-10-06. A Hierarchical Variational Neural Uncertainty Model for Stochastic Video Prediction. https://arxiv.org/abs/2110.03446

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