Keywords: disentanglement, spatiotemporal prediction, representation learning, dynamical systems, separation of variables
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.
One-sentence Summary: We introduce a novel interpretation of spatiotemporal disentanglement, inducing a simple and performant disentangled prediction model.
Supplementary Material: zip
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Code: [![github](/images/github_icon.svg) JeremDona/spatiotemporal_variable_separation](https://github.com/JeremDona/spatiotemporal_variable_separation)
Data: [Chairs](https://paperswithcode.com/dataset/chairs), [MNIST](https://paperswithcode.com/dataset/mnist), [Moving MNIST](https://paperswithcode.com/dataset/moving-mnist)
Community Implementations: [![CatalyzeX](/images/catalyzex_icon.svg) 1 code implementation](https://www.catalyzex.com/paper/arxiv:2008.01352/code)