# Generative Adversarial Minority Oversampling

## TL;DR

This paper introduces a novel adversarial oversampling method for deep learning that generates synthetic minority class samples to improve classification performance on imbalanced datasets.

## Contribution

It proposes a three-player adversarial framework involving a convex generator, classifier, and discriminator to generate effective minority class samples in deep learning.

## Key findings

- Improves minority class classification accuracy
- Generates samples near class boundaries
- Effective on multiple imbalanced image datasets

## Abstract

Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning. Oversampling techniques, which are effective for handling class imbalance in classical learning systems, can not be directly applied to end-to-end deep learning systems. We propose a three-player adversarial game between a convex generator, a multi-class classifier network, and a real/fake discriminator to perform oversampling in deep learning systems. The convex generator generates new samples from the minority classes as convex combinations of existing instances, aiming to fool both the discriminator as well as the classifier into misclassifying the generated samples. Consequently, the artificial samples are generated at critical locations near the peripheries of the classes. This, in turn, adjusts the classifier induced boundaries in a way which is more likely to reduce misclassification from the minority classes. Extensive experiments on multiple class imbalanced image datasets establish the efficacy of our proposal.