Megan: Mixture of experts of generative adversarial networks for multimodal image generation

David Keetae Park, Seungjoo Yoo, Hyojin Bahng, Jaegul Choo, Noseong Park

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Citation (Scopus)

Abstract

Recently, generative adversarial networks (GANs) have shown promising performance in generating realistic images. However, they often struggle in learning complex underlying modalities in a given dataset, resulting in poor-quality generated images. To mitigate this problem, we present a novel approach called mixture of experts GAN (MEGAN), an ensemble approach of multiple generator networks. Each generator network in MEGAN specializes in generating images with a particular subset of modalities, e.g., an image class. Instead of incorporating a separate step of handcrafted clustering of multiple modalities, our proposed model is trained through an end-to-end learning of multiple generators via gating networks, which is responsible for choosing the appropriate generator network for a given condition. We adopt the categorical reparameterization trick for a categorical decision to be made in selecting a generator while maintaining the flow of the gradients. We demonstrate that individual generators learn different and salient subparts of the data and achieve a multiscale structural similarity (MS-SSIM) score of 0.2470 for CelebA and a competitive unsupervised inception score of 8.33 in CIFAR-10.

Original languageEnglish
Title of host publicationProceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018
EditorsJerome Lang
PublisherInternational Joint Conferences on Artificial Intelligence
Pages878-884
Number of pages7
ISBN (Electronic)9780999241127
DOIs
Publication statusPublished - 2018
Event27th International Joint Conference on Artificial Intelligence, IJCAI 2018 - Stockholm, Sweden
Duration: 2018 Jul 132018 Jul 19

Publication series

NameIJCAI International Joint Conference on Artificial Intelligence
Volume2018-July
ISSN (Print)1045-0823

Other

Other27th International Joint Conference on Artificial Intelligence, IJCAI 2018
CountrySweden
CityStockholm
Period18/7/1318/7/19

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence

Cite this

Park, D. K., Yoo, S., Bahng, H., Choo, J., & Park, N. (2018). Megan: Mixture of experts of generative adversarial networks for multimodal image generation. In J. Lang (Ed.), Proceedings of the 27th International Joint Conference on Artificial Intelligence, IJCAI 2018 (pp. 878-884). (IJCAI International Joint Conference on Artificial Intelligence; Vol. 2018-July). International Joint Conferences on Artificial Intelligence. https://doi.org/10.24963/ijcai.2018/122