Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

PDE-Driven Spatiotemporal Disentanglement

Abstract : A recent line of work 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 to introduce a dynamical interpretation of spatiotemporal disentanglement. It induces a simple and 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.
Complete list of metadatas

Cited literature [73 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-02911067
Contributor : Jean-Yves Franceschi <>
Submitted on : Monday, August 3, 2020 - 5:36:52 PM
Last modification on : Wednesday, August 5, 2020 - 10:20:02 PM

Files

var_sep.pdf
Files produced by the author(s)

Licence


Distributed under a Creative Commons Attribution 4.0 International License

Identifiers

  • HAL Id : hal-02911067, version 1
  • ARXIV : 2008.01352

Citation

Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier, Patrick Gallinari. PDE-Driven Spatiotemporal Disentanglement. 2020. ⟨hal-02911067⟩

Share

Metrics

Record views

72

Files downloads

26