A Factorial Mixture Prior for Compositional Deep Generative Models

Ulrich Paquet; Sumedh K. Ghaisas; Olivier Tieleman

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arXiv · 1812.07480

A Factorial Mixture Prior for Compositional Deep Generative Models

Ulrich Paquet·Sumedh K. Ghaisas·Olivier Tieleman

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Abstract

We assume that a high-dimensional datum, like an image, is a compositional expression of a set of properties, with a complicated non-linear relationship between the datum and its properties. This paper proposes a factorial mixture prior for capturing latent properties, thereby adding structured compositionality to deep generative models. The prior treats a latent vector as belonging to Cartesian product of subspaces, each of which is quantized separately with a Gaussian mixture model. Some mixture components can be set to represent properties as observed random variables whenever labeled properties are present. Through a combination of stochastic variational inference and gradient descent, a method for learning how to infer discrete properties in an unsupervised or semi-supervised way is outlined and empirically evaluated.

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BibTeX RIS

Ulrich Paquet, Sumedh K. Ghaisas, Olivier Tieleman. 2018-12-18. A Factorial Mixture Prior for Compositional Deep Generative Models. https://arxiv.org/abs/1812.07480

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