  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.
The generative model learns useful features from majority classes and uses
these to generate images for minority classes. We apply class conditioning in
the latent space to drive the generation process towards a target class. The
generator in the GAN is initialized with the encoder module of an autoencoder
that enables us to learn an accurate class-conditioning in the latent space. We
compare the proposed methodology with state-of-the-art GANs and demonstrate
that BAGAN generates images of superior quality when trained with an imbalanced
dataset.
