Web: http://arxiv.org/abs/2202.03169

June 16, 2022, 1:11 a.m. | Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M. Asano, Taco Cohen, Efstratios Gavves

cs.LG updates on arXiv.org arxiv.org

Understanding the latent causal factors of a dynamical system from visual
observations is considered a crucial step towards agents reasoning in complex
environments. In this paper, we propose CITRIS, a variational autoencoder
framework that learns causal representations from temporal sequences of images
in which underlying causal factors have possibly been intervened upon. In
contrast to the recent literature, CITRIS exploits temporality and observing
intervention targets to identify scalar and multidimensional causal factors,
such as 3D rotation angles. Furthermore, by introducing …

arxiv lg temporal

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