Mixed Membership Generative Adversarial Networks

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Mixed Membership Generative Adversarial Networks
Title:
Mixed Membership Generative Adversarial Networks
Other Titles:
2022 IEEE International Conference on Image Processing (ICIP)
Keywords:
Publication Date:
18 October 2022
Citation:
Yazici, Y., Lecouat, B., Yap, K. H., Winkler, S., Piliouras, G., Chandrasekhar, V., & Foo, C.-S. (2022). Mixed Membership Generative Adversarial Networks. 2022 IEEE International Conference on Image Processing (ICIP). https://doi.org/10.1109/icip46576.2022.9897640
Abstract:
GANs are designed to learn a single distribution, though multiple distributions can be modeled by treating them separately. However, this naive implementation does not consider overlapping distributions. We propose Mixed Membership Generative Adversarial Networks (MMGAN) analogous to mixed-membership models that model multiple distributions and discover their commonalities and particularities. Each data distribution is modeled as a mixture over a common set of generator distributions, and mixture weights are automatically learned from the data. Mixture weights can give insight into common and unique features of each data distribution. We evaluate our proposed MMGAN and show its effectiveness on MNIST and Fashion-MNIST with various settings.
License type:
Publisher Copyright
Funding Info:
This research / project is supported by the A*STAR - AME Programmatic Funds
Grant Reference no. : A20H6b0151
Description:
© 2022 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
ISSN:
2381-8549
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