BAGAN: Data Augmentation with Balancing GAN

Understand

Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers.

• In this work we propose balancing GAN (BAGAN) as an augmentation tool to restore balance in imbalanced datasets.
• This is challenging because the few minority-class images may not be enough to train a GAN.
• We overcome this issue by including during the adversarial training all available images of majority and minority classes.