Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: SharinGAN: Combining Synthetic and Real Data for Unsupervised Geometry
  Estimation   We propose a novel method for combining synthetic and real images when
training networks to determine geometric information from a single image. We
suggest a method for mapping both image types into a single, shared domain.
This is connected to a primary network for end-to-end training. Ideally, this
results in images from two domains that present shared information to the
primary network. Our experiments demonstrate significant improvements over the
state-of-the-art in two important domains, surface normal estimation of human
faces and monocular depth estimation for outdoor scenes, both in an
unsupervised setting.

27
The categories of this paper are: comp-gas
29
The categories of this paper are: funct-an
31
The categories of this paper are: ao-sci
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
17
The categories of this paper are: q-fin
14
The categories of this paper are: math-ph
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
22
The categories of this paper are: chao-dyn
35
The categories of this paper are: mtrl-th
34
The categories of this paper are: chem-ph
19
The categories of this paper are: eess
15
The categories of this paper are: nucl-ex
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an