Related papers: Inverse Learning of Symmetries

Inverse Learning of Symmetries

URL: http://arxiv.org/abs/2002.02782v2
Date: Thu, 22 Oct 2020 14:46:53 GMT
Title: Inverse Learning of Symmetries
Authors: Mario Wieser, Sonali Parbhoo, Aleksander Wieczorek, Volker Roth
Abstract summary: We learn the symmetry transformation with a model consisting of two latent subspaces. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Our model outperforms state-of-the-art methods on artificial and molecular datasets.
Score: 71.62109774068064
License: http://creativecommons.org/licenses/by-nc-sa/4.0/
Abstract: Symmetry transformations induce invariances which are frequently described with deep latent variable models. In many complex domains, such as the chemical space, invariances can be observed, yet the corresponding symmetry transformation cannot be formulated analytically. We propose to learn the symmetry transformation with a model consisting of two latent subspaces, where the first subspace captures the target and the second subspace the remaining invariant information. Our approach is based on the deep information bottleneck in combination with a continuous mutual information regulariser. Unlike previous methods, we focus on the challenging task of minimising mutual information in continuous domains. To this end, we base the calculation of mutual information on correlation matrices in combination with a bijective variable transformation. Extensive experiments demonstrate that our model outperforms state-of-the-art methods on artificial and molecular datasets.

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