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PDE-Driven Spatiotemporal Disentanglement

Abstract : A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differential equations theory. Following this direction, we propose in this article a novel and general paradigm for this task based on a resolution method for partial differential equations: the separation of variables. This inspiration allows us to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a principled model based on learning disentangled spatial and temporal representations of a phenomenon to accurately predict future observations. We experimentally demonstrate the performance and broad applicability of our method against prior state-of-the-art models on physical and synthetic video datasets.
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https://hal.archives-ouvertes.fr/hal-02911067
Contributor : Jean-Yves Franceschi Connect in order to contact the contributor
Submitted on : Wednesday, March 17, 2021 - 11:23:21 PM
Last modification on : Monday, June 14, 2021 - 10:41:19 AM

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Distributed under a Creative Commons Attribution 4.0 International License

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  • HAL Id : hal-02911067, version 3
  • ARXIV : 2008.01352

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Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari. PDE-Driven Spatiotemporal Disentanglement. The Ninth International Conference on Learning Representations, International Conference on Representation Learning, May 2021, Vienne, Austria. ⟨hal-02911067v3⟩

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