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Poster
A Disentangled Recognition and Nonlinear Dynamics Model for Unsupervised Learning
Marco Fraccaro · Simon Kamronn · Ulrich Paquet · Ole Winther

Wed Dec 06 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #176
in Posters Wed »

This paper takes a step towards temporal reasoning in a dynamically changing video, not in the pixel space that constitutes its frames, but in a latent space that describes the non-linear dynamics of the objects in its world. We introduce the Kalman variational auto-encoder, a framework for unsupervised learning of sequential data that disentangles two latent representations: an object's representation, coming from a recognition model, and a latent state describing its dynamics. As a result, the evolution of the world can be imagined and missing data imputed, both without the need to generate high dimensional frames at each time step. The model is trained end-to-end on videos of a variety of simulated physical systems, and outperforms competing methods in generative and missing data imputation tasks.

Keywords: Variational Inference · Unsupervised Learning · Deep Autoencoders · Generative Models
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Author Information

Marco Fraccaro (Technical University of Denmark (DTU))
Simon Kamronn (Technical University of Denmark)

Deep learning and Bayesian statistical modelling of time series from a long-term intervention study.

Ulrich Paquet
Ole Winther (Technical University of Denmark)

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