Inducing Interpretable Representations with Variational Autoencoders

N. Siddharth; Brooks Paige; Alban Desmaison; Jan-Willem Van de Meent; Frank Wood; Noah D. Goodman; Pushmeet Kohli; Philip H. S. Torr

arXiv · 1611.07492

Inducing Interpretable Representations with Variational Autoencoders

N. Siddharth·Brooks Paige·Alban Desmaison·Jan-Willem Van de Meent·Frank Wood·Noah D. Goodman·Pushmeet Kohli·Philip H. S. Torr

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Abstract

We develop a framework for incorporating structured graphical models in the \emph{encoders} of variational autoencoders (VAEs) that allows us to induce interpretable representations through approximate variational inference. This allows us to both perform reasoning (e.g. classification) under the structural constraints of a given graphical model, and use deep generative models to deal with messy, high-dimensional domains where it is often difficult to model all the variation. Learning in this framework is carried out end-to-end with a variational objective, applying to both unsupervised and semi-supervised schemes.

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N. Siddharth, Brooks Paige, Alban Desmaison, Jan-Willem Van de Meent, Frank Wood, Noah D. Goodman, Pushmeet Kohli, Philip H. S. Torr. 2016-11-22. Inducing Interpretable Representations with Variational Autoencoders. https://arxiv.org/abs/1611.07492

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