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arXiv · 1802.04920

DVAE++: Discrete Variational Autoencoders with Overlapping Transformations

Arash Vahdat·William G. Macready·Zhengbing Bian·Amir Khoshaman·Evgeny Andriyash
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Abstract

Training of discrete latent variable models remains challenging because passing gradient information through discrete units is difficult. We propose a new class of smoothing transformations based on a mixture of two overlapping distributions, and show that the proposed transformation can be used for training binary latent models with either directed or undirected priors. We derive a new variational bound to efficiently train with Boltzmann machine priors. Using this bound, we develop DVAE++, a generative model with a global discrete prior and a hierarchy of convolutional continuous variables. Experiments on several benchmarks show that overlapping transformations outperform other recent continuous relaxations of discrete latent variables including Gumbel-Softmax (Maddison et al., 2016; Jang et al., 2016), and discrete variational autoencoders (Rolfe 2016).

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Arash Vahdat, William G. Macready, Zhengbing Bian, Amir Khoshaman, Evgeny Andriyash. 2018-02-14. DVAE++: Discrete Variational Autoencoders with Overlapping Transformations. https://arxiv.org/abs/1802.04920

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