Dirichlet Variational Autoencoder

doi:10.1016/j.patcog.2020.107514

2020-11-01

Weonyoung Joo

Wonsung Lee

Il‐Chul Moon *

DOI：10.1016/j.patcog.2020.107514

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This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the inverse cumulative distribution function of the Gamma distribution, which is a component of the Dirichlet distribution. This approximation on a new prior led an investigation on the component collapsing, and DirVAE revealed that the component collapsing originates from two problem sources: decoder weight collapsing and latent value collapsing. The experimental results show that 1) DirVAE generates the result with the best log-likelihood compared to the baselines; 2) DirVAE produces more interpretable latent values with no collapsing issues which the baselines suffer from; 3) the latent representation from DirVAE achieves the best classification accuracy in the (semi-)supervised classification tasks on MNIST, OMNIGLOT, COIL-20, SVHN, and CIFAR-10 compared to the baseline VAEs; and 4) the DirVAE augmented topic models show better performances in most cases. (C) 2020 Elsevier Ltd. All rights reserved.

Keyword:

Representation learning

Variational autoencoder

Deep generative model

Multi-modal latent representation

Component collapse