Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Twin-GAN -- Unpaired Cross-Domain Image Translation with Weight-Sharing
  GANs   We present a framework for translating unlabeled images from one domain into
analog images in another domain. We employ a progressively growing
skip-connected encoder-generator structure and train it with a GAN loss for
realistic output, a cycle consistency loss for maintaining same-domain
translation identity, and a semantic consistency loss that encourages the
network to keep the input semantic features in the output. We apply our
framework on the task of translating face images, and show that it is capable
of learning semantic mappings for face images with no supervised one-to-one
image mapping.

37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
0
The categories of this paper are: hep-ph
11
The categories of this paper are: hep-lat
3
The categories of this paper are: physics
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
20
The categories of this paper are: acc-phys
18
The categories of this paper are: econ
21
The categories of this paper are: adap-org
27
The categories of this paper are: comp-gas
24
The categories of this paper are: dg-ga
12
The categories of this paper are: nucl-th