Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: OneGAN: Simultaneous Unsupervised Learning of Conditional Image
  Generation, Foreground Segmentation, and Fine-Grained Clustering   We present a method for simultaneously learning, in an unsupervised manner,
(i) a conditional image generator, (ii) foreground extraction and segmentation,
(iii) clustering into a two-level class hierarchy, and (iv) object removal and
background completion, all done without any use of annotation. The method
combines a Generative Adversarial Network and a Variational Auto-Encoder, with
multiple encoders, generators and discriminators, and benefits from solving all
tasks at once. The input to the training scheme is a varied collection of
unlabeled images from the same domain, as well as a set of background images
without a foreground object. In addition, the image generator can mix the
background from one image, with a foreground that is conditioned either on that
of a second image or on the index of a desired cluster. The method obtains
state of the art results in comparison to the literature methods, when compared
to the current state of the art in each of the tasks.

18
The categories of this paper are: econ
12
The categories of this paper are: nucl-th
33
The categories of this paper are: plasm-ph
31
The categories of this paper are: ao-sci
6
The categories of this paper are: astro-ph
36
The categories of this paper are: cmp-lg
5
The categories of this paper are: gr-qc
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
9
The categories of this paper are: nlin
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
16
The categories of this paper are: stat
19
The categories of this paper are: eess
26
The categories of this paper are: bayes-an
23
The categories of this paper are: solv-int