MGAN: training generative adversarial nets with multiple generators

Quan Hoang, Tu Dinh Nguyen, Trung Le, Dinh Phung

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

137 Citations (Scopus)


We propose in this paper a new approach to train the Generative Adversarial Nets (GANs) with a mixture of generators to overcome the mode collapsing problem. The main intuition is to employ multiple generators, instead of using a single one as in the original GAN. The idea is simple, yet proven to be extremely effective at covering diverse data modes, easily overcoming the mode collapsing problem and delivering state-of-the-art results. A minimax formulation was able to establish among a classifier, a discriminator, and a set of generators in a similar spirit with GAN. Generators create samples that are intended to come from the same distribution as the training data, whilst the discriminator determines whether samples are true data or generated by generators, and the classifier specifies which generator a sample comes from. The distinguishing feature is that internal samples are created from multiple generators, and then one of them will be randomly selected as final output similar to the mechanism of a probabilistic mixture model. We term our method Mixture Generative Adversarial Nets (MGAN). We develop theoretical analysis to prove that, at the equilibrium, the Jensen-Shannon divergence (JSD) between the mixture of generators’ distributions and the empirical data distribution is minimal, whilst the JSD among generators’ distributions is maximal, hence effectively avoiding the mode collapsing problem. By utilizing parameter sharing, our proposed model adds minimal computational cost to the standard GAN, and thus can also efficiently scale to large-scale datasets. We conduct extensive experiments on synthetic 2D data and natural image databases (CIFAR-10, STL-10 and ImageNet) to demonstrate the superior performance of our MGAN in achieving state-of-the-art Inception scores over latest baselines, generating diverse and appealing recognizable objects at different resolutions, and specializing in capturing different types of objects by the generators.
Original languageEnglish
Title of host publication6th International Conference on Learning Representations, ICLR 2018
EditorsIain Murray, Marc’Aurelio Ranzato, Oriol Vinyals
Place of PublicationAmherst MA USA
Number of pages24
Publication statusPublished - 2018
Externally publishedYes
EventInternational Conference on Learning Representations 2018 - Vancouver, Canada
Duration: 30 Apr 20183 May 2018
Conference number: 6th (Proceedings)


ConferenceInternational Conference on Learning Representations 2018
Abbreviated titleICLR 2018
Internet address


  • GANs
  • Mode Collapse
  • Mixture
  • Jensen-Shannon Divergence
  • Inception Score
  • Generator
  • Discriminator
  • CIFAR-10
  • STL-10
  • ImageNet

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