  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.
