Timezone: »

 
Poster
Multi-marginal Wasserstein GAN
Jiezhang Cao · Langyuan Mo · Yifan Zhang · Kui Jia · Chunhua Shen · Mingkui Tan

Tue Dec 10 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #13
in Algorithms -- Adversarial Learning »

Multiple marginal matching problem aims at learning mappings to match a source domain to multiple target domains and it has attracted great attention in many applications, such as multi-domain image translation. However, addressing this problem has two critical challenges: (i) Measuring the multi-marginal distance among different domains is very intractable; (ii) It is very difficult to exploit cross-domain correlations to match the target domain distributions. In this paper, we propose a novel Multi-marginal Wasserstein GAN (MWGAN) to minimize Wasserstein distance among domains. Specifically, with the help of multi-marginal optimal transport theory, we develop a new adversarial objective function with inner- and inter-domain constraints to exploit cross-domain correlations. Moreover, we theoretically analyze the generalization performance of MWGAN, and empirically evaluate it on the balanced and imbalanced translation tasks. Extensive experiments on toy and real-world datasets demonstrate the effectiveness of MWGAN.

Keywords: Generative Models · Algorithms · Adversarial Learning · Applications -> Computer Vision; Deep Learning -> Adversarial Networks; Deep Learning

Author Information

Jiezhang Cao (South China University of Technology)
Langyuan Mo (South China University of Technology)
Yifan Zhang (South China University of Technology)
Kui Jia (South China University of Technology)
Chunhua Shen (University of Adelaide)
Mingkui Tan (South China University of Technology)

More from the Same Authors