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  Intrinsic disentanglement: an invariance view for deep generative models

Besserve, M., Sun, R., & Schölkopf, B. (2018). Intrinsic disentanglement: an invariance view for deep generative models. In ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models. Retrieved from https://sites.google.com/view/tadgm/accepted-papers.

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 Creators:
Besserve, M1, 2, Author              
Sun, R, Author
Schölkopf, B3, Author              
Affiliations:
1Department Physiology of Cognitive Processes, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497798              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, Spemannstrasse 38, 72076 Tübingen, DE, ou_1497794              
3Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Deep generative models such as Generative Ad- versarial Networks (GANs) and Variational Auto- Encoders (VAEs) are important tools to capture and investigate the properties of complex empiri- cal data. However, the complexity of their inner elements makes their functioning challenging to interpret and modify. In this respect, these archi- tectures behave as black box models. In order to better understand the function of such network, we analyze the modularity of these system by quantifying the disentanglement of their intrinsic parameters. This concept relates to a notion of invariance to transformations of internal variables of the generative model, recently introduced in the field of causality. Our experiments on generation of human faces with VAEs supports that modu- larity between weights distributed over layers of generator architecture is achieved to some degree, and can be used to understand better the function- ing of these architectures. Finally, we show that modularity can be enhanced during optimization.

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Language(s): eng - English
 Dates: 2018-07
 Publication Status: Published online
 Pages: 9
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Title: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models
Place of Event: Stockholm, Sweden
Start-/End Date: 2018-07-14 - 2018-07-15

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Title: ICML 2018 Workshop on Theoretical Foundations and Applications of Deep Generative Models
Source Genre: Proceedings
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