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Learning discrete and continuous factors of data via alternating disentanglement

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Issue Date
2019-01
Publisher
International Machine Learning Society (IMLS)
Citation
36th International Conference on Machine Learning, ICML 2019, Vol.2019-June, pp.5441-5449
Abstract
© 36th International Conference on Machine Learning, ICML 2019. All rights reserved.We address the problem of unsupervised disentanglement of discrete and continuous explanatory factors of data. We first show a simple procedure for minimizing the total correlation of the continuous latent variables without having to use a discriminator network or perform importance sampling, via cascading the information flow in the β-vae framework. Furthermore, we propose a method which avoids offloading the entire burden of jointly modeling the continuous and discrete factors to the variational encoder by employing a separate discrete inference procedure. This leads to an interesting alternating minimization problem which switches between finding the most likely discrete configuration given the continuous factors and updating the variational encoder based on the computed discrete factors. Experiments show that the proposed method clearly disentangles discrete factors and significantly outperforms current disentanglement methods based on the disentanglement score and inference network classification score. The source code is available at https://github.com/snumllab/DisentanglementICML 19.
URI
https://hdl.handle.net/10371/179337
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College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Computer Science and Engineering (컴퓨터공학부)Journal Papers (저널논문_컴퓨터공학부)
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