{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T09:26:53Z","timestamp":1780392413906,"version":"3.54.1"},"publisher-location":"California","reference-count":0,"publisher":"International Joint Conferences on Artificial Intelligence Organization","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2018,7]]},"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.24963\/ijcai.2018\/122","type":"proceedings-article","created":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:49:10Z","timestamp":1530755350000},"page":"878-884","source":"Crossref","is-referenced-by-count":16,"title":["MEGAN: Mixture of Experts of Generative Adversarial Networks for Multimodal Image Generation"],"prefix":"10.24963","author":[{"given":"David Keetae","family":"Park","sequence":"first","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seungjoo","family":"Yoo","sequence":"additional","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hyojin","family":"Bahng","sequence":"additional","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaegul","family":"Choo","sequence":"additional","affiliation":[{"name":"Korea University"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Noseong","family":"Park","sequence":"additional","affiliation":[{"name":"University of North Carolina at Charlotte"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"10584","event":{"name":"Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}","theme":"Artificial Intelligence","location":"Stockholm, Sweden","acronym":"IJCAI-2018","number":"27","sponsor":["International Joint Conferences on Artificial Intelligence Organization (IJCAI)"],"start":{"date-parts":[[2018,7,13]]},"end":{"date-parts":[[2018,7,19]]}},"container-title":["Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence"],"original-title":[],"deposited":{"date-parts":[[2018,7,5]],"date-time":"2018-07-05T01:50:07Z","timestamp":1530755407000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.ijcai.org\/proceedings\/2018\/122"}},"subtitle":[],"proceedings-subject":"Artificial Intelligence Research Articles","short-title":[],"issued":{"date-parts":[[2018,7]]},"references-count":0,"URL":"https:\/\/doi.org\/10.24963\/ijcai.2018\/122","relation":{},"subject":[],"published":{"date-parts":[[2018,7]]}}}