Metric-based generative adversarial network

Guoxian Dai, Jin Xie, Yi Fang

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


Existing methods of generative adversarial network (GAN) use different criteria to distinguish between real and fake samples, such as probability [9], energy [44] or other losses [30]. In this paper, by employing the merits of deep metric learning, we propose a novel metric-based generative adversarial network (MBGAN), which uses the distance-criteria to distinguish between real and fake samples. Specifically, the discriminator of MBGAN adopts a triplet structure and learns a deep nonlinear transformation, which maps input samples into a new feature space. In the transformed space, the distance between real samples is minimized, while the distance between real sample and fake sample is maximized. Similar to the adversarial procedure of existing GANs, a generator is trained to produce synthesized examples, which are close to real examples, while a discriminator is trained to maximize the distance between real and fake samples to a large margin. Meanwhile, instead of using a fixed margin, we adopt a data-dependent margin [30], so that the generator could focus on improving the synthesized samples with poor quality, instead of wasting energy on well-produce samples. Our proposed method is verified on various benchmarks, such as CIFAR-10, SVHN and CelebA, and generates high-quality samples.

Original languageEnglish (US)
Title of host publicationMM 2017 - Proceedings of the 2017 ACM Multimedia Conference
PublisherAssociation for Computing Machinery, Inc
Number of pages9
ISBN (Electronic)9781450349062
StatePublished - Oct 23 2017
Event25th ACM International Conference on Multimedia, MM 2017 - Mountain View, United States
Duration: Oct 23 2017Oct 27 2017

Publication series

NameMM 2017 - Proceedings of the 2017 ACM Multimedia Conference


Other25th ACM International Conference on Multimedia, MM 2017
Country/TerritoryUnited States
CityMountain View


  • Data-dependent margin
  • Deep metric learning
  • Generative adversarial network

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design
  • Media Technology
  • Computer Vision and Pattern Recognition
  • Software


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