Dirichlet Variational Autoencoder

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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.
Publisher
ELSEVIER SCI LTD
Issue Date
2020-11
Language
English
Article Type
Article
Citation

PATTERN RECOGNITION, v.107

ISSN
0031-3203
DOI
10.1016/j.patcog.2020.107514
URI
http://hdl.handle.net/10203/281842
Appears in Collection
IE-Journal Papers(저널논문)
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