Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Multi-class Generative Adversarial Nets for Semi-supervised Image
  Classification   From generating never-before-seen images to domain adaptation, applications
of Generative Adversarial Networks (GANs) spread wide in the domain of vision
and graphics problems. With the remarkable ability of GANs in learning the
distribution and generating images of a particular class, they can be used for
semi-supervised classification tasks. However, the problem is that if two
classes of images share similar characteristics, the GAN might learn to
generalize and hinder the classification of the two classes. In this paper, we
use various images from MNIST and Fashion-MNIST datasets to illustrate how
similar images cause the GAN to generalize, leading to the poor classification
of images. We propose a modification to the traditional training of GANs that
allows for improved multi-class classification in similar classes of images in
a semi-supervised learning framework.

11
The categories of this paper are: hep-lat
6
The categories of this paper are: astro-ph
3
The categories of this paper are: physics
9
The categories of this paper are: nlin
17
The categories of this paper are: q-fin
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
28
The categories of this paper are: alg-geom
34
The categories of this paper are: chem-ph
15
The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
5
The categories of this paper are: gr-qc
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
33
The categories of this paper are: plasm-ph
30
The categories of this paper are: q-alg