Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: cGANs with Multi-Hinge Loss   We propose a new algorithm to incorporate class conditional information into
the critic of GANs via a multi-class generalization of the commonly used Hinge
loss that is compatible with both supervised and semi-supervised settings. We
study the compromise between training a state of the art generator and an
accurate classifier simultaneously, and propose a way to use our algorithm to
measure the degree to which a generator and critic are class conditional. We
show the trade-off between a generator-critic pair respecting class
conditioning inputs and generating the highest quality images. With our
multi-hinge loss modification we are able to improve Inception Scores and
Frechet Inception Distance on the Imagenet dataset. We make our tensorflow code
available at https://github.com/ilyakava/gan.

37
The categories of this paper are: supr-con
13
The categories of this paper are: quant-ph
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
18
The categories of this paper are: econ
27
The categories of this paper are: comp-gas
12
The categories of this paper are: nucl-th
35
The categories of this paper are: mtrl-th
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
34
The categories of this paper are: chem-ph
4
The categories of this paper are: cond-mat
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
10
The categories of this paper are: q-bio