Dual discriminator generative adversarial nets

Tu Dinh Nguyen, Trung Le, Hung Vu, Dinh Phung

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

69 Citations (Scopus)

Abstract

We propose in this paper a novel approach to tackle the problem of mode collapse encountered in generative adversarial network (GAN). Our idea is intuitive but proven to be very effective, especially in addressing some key limitations of GAN. In essence, it combines the Kullback-Leibler (KL) and reverse KL divergences into a unified objective function, thus it exploits the complementary statistical properties from these divergences to effectively diversify the estimated density in capturing multi-modes. We term our method dual discriminator generative adversarial nets (D2GAN) which, unlike GAN, has two discriminators; and together with a generator, it also has the analogy of a minimax game, wherein a discriminator rewards high scores for samples from data distribution whilst another discriminator, conversely, favoring data from the generator, and the generator produces data to fool both two discriminators. We develop theoretical analysis to show that, given the maximal discriminators, optimizing the generator of D2GAN reduces to minimizing both KL and reverse KL divergences between data distribution and the distribution induced from the data generated by the generator, hence effectively avoiding the mode collapsing problem. We conduct extensive experiments on synthetic and real-world large-scale datasets (MNIST, CIFAR-10, STL-10, ImageNet), where we have made our best effort to compare our D2GAN with the latest state-of-the-art GAN's variants in comprehensive qualitative and quantitative evaluations. The experimental results demonstrate the competitive and superior performance of our approach in generating good quality and diverse samples over baselines, and the capability of our method to scale up to ImageNet database.

Original languageEnglish
Title of host publicationAdvances in Neural Information Processing Systems 30 (NIPS 2017)
EditorsI. Guyon, U.V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, R. Garnett
Place of PublicationSan Diego CA USA
PublisherNeural Information Processing Systems (NIPS)
Number of pages11
Volume30
Publication statusPublished - 2017
Externally publishedYes
EventAdvances in Neural Information Processing Systems 2017 - Long Beach, United States of America
Duration: 4 Dec 20179 Dec 2017
Conference number: 30th
https://dl.acm.org/doi/proceedings/10.5555/3295222 (Proceedings)

Publication series

NameAdvances in Neural Information Processing Systems
PublisherNeural Information Processing Systems (NIPS)
ISSN (Print)1049-5258

Conference

ConferenceAdvances in Neural Information Processing Systems 2017
Abbreviated titleNIPS 2017
CountryUnited States of America
CityLong Beach
Period4/12/179/12/17
Internet address

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