Corpus ID: 211252522

Stochastic Latent Residual Video Prediction

@inproceedings{Franceschi2020StochasticLR,
  title={Stochastic Latent Residual Video Prediction},
  author={Jean-Yves Franceschi and Edouard Delasalles and Mickael Chen and Sylvain Lamprier and P. Gallinari},
  booktitle={ICML},
  year={2020}
}
Designing video prediction models that account for the inherent uncertainty of the future is challenging. Most works in the literature are based on stochastic image-autoregressive recurrent networks, which raises several performance and applicability issues. An alternative is to use fully latent temporal models which untie frame synthesis and temporal dynamics. However, no such model for stochastic video prediction has been proposed in the literature yet, due to design and training difficulties… Expand
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