Learning Generative Adversarial Networks from multiple data sources

Trung Le, Quan Hoang, Hung Vu, Tu Dinh Nguyen, Hung Bui, Dinh Phung

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3 Citations (Scopus)


Generative Adversarial Networks (GANs) are a powerful class of deep generative models. In this paper, we extend GAN to the problem of generating data that are not only close to a primary data source but also required to be different from auxiliary data sources. For this problem, we enrich both GAN's formulations and applications by introducing pushing forces that thrust generated samples away from given auxiliary data sources. We term our method Push-and-Pull GAN (P2GAN). We conduct extensive experiments to demonstrate the merit of P2GAN in two applications: generating data with constraints and addressing the mode collapsing problem. We use CIFAR-10, STL-10, and ImageNet datasets and compute Fréchet Inception Distance to evaluate P2GAN's effectiveness in addressing the mode collapsing problem. The results show that P2GAN outperforms the state-of-the-art baselines. For the problem of generating data with constraints, we show that P2GAN can successfully avoid generating specific features such as black hair.

Original languageEnglish
Title of host publicationProceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence
EditorsSarit Kraus
Place of PublicationMarina del Rey CA USA
PublisherAssociation for the Advancement of Artificial Intelligence (AAAI)
Number of pages7
ISBN (Electronic)9780999241141
Publication statusPublished - 2019
EventInternational Joint Conference on Artificial Intelligence 2019 - Macao, China
Duration: 10 Aug 201916 Aug 2019
Conference number: 28th
https://www.ijcai.org/proceedings/2019/ (Proceedings)


ConferenceInternational Joint Conference on Artificial Intelligence 2019
Abbreviated titleIJCAI 2019
Internet address


  • Machine Learning
  • Learning Generative Models
  • Adversarial Machine Learning

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