Adversarial Classifier for Imbalanced Problems

Understand

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.