  Adversarial approach has been widely used for data generation in the last few
years. However, this approach has not been extensively utilized for classifier
training. In this paper, we propose an adversarial framework for classifier
training that can also handle imbalanced data. Indeed, a network is trained via
an adversarial approach to give weights to samples of the majority class such
that the obtained classification problem becomes more challenging for the
discriminator and thus boosts its classification capability. In addition to the
general imbalanced classification problems, the proposed method can also be
used for problems such as graph representation learning in which it is desired
to discriminate similar nodes from dissimilar nodes. Experimental results on
imbalanced data classification and on the tasks like graph link prediction show
the superiority of the proposed method compared to the state-of-the-art
methods.
