Dirichlet Variational Autoencoder

By Weonyoung Joo, Wonsung Lee, Sungrae Park, Il-Chul Moon. 2019

123 citations recorded by the source corpus.

This paper proposes Dirichlet Variational Autoencoder (DirVAE) using a Dirichlet prior for a continuous latent variable that exhibits the characteristic of the categorical probabilities. To infer the parameters of DirVAE, we utilize the stochastic gradient method by approximating the Gamma distribution, which is a component of the Dirichlet distribution, with the inverse Gamma CDF approximation. Additionally, we reshape the component collapsing issue by investigating two problem sources, which are decoder weight collapsing and latent value collapsing, and we show that DirVAE has no component collapsing; while Gaussian VAE exhibits the decoder weight collapsing and Stick-Breaking VAE shows the latent value collapsing. The experimental results show that 1) DirVAE models the latent representation result with the best log-likelihood compared to the baselines; and 2) DirVAE produces more interpretable latent values with no collapsing issues which the baseline models suffer from. Also, we show that the learned latent representation from the DirVAE achieves the best classification accuracy in the semi-supervised and the supervised classification tasks on MNIST, OMNIGLOT, and SVHN compared to the baseline VAEs. Finally, we demonstrated that the DirVAE augmented topic models show better performances in most cases.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

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Citations in the corpus

Continual Unsupervised Representation Learning (2019, methodology): “We utilise the same architecture and hyperparameter settings as in Joo et al. (2019) for consistency, with latent spaces of dimension 50 and 100 for MNIST and Omniglot respectively; and full details of the experimental setup can be found in Appendix C.3.”