Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-marginal Wasserstein GAN   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.

24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
25
The categories of this paper are: patt-sol
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
7
The categories of this paper are: hep-th
2
The categories of this paper are: math
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
23
The categories of this paper are: solv-int
22
The categories of this paper are: chao-dyn
36
The categories of this paper are: cmp-lg
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys