  Class imbalance is an inherent problem in many machine learning
classification tasks. This often leads to trained models that are unusable for
any practical purpose. In this study we explore an unsupervised approach to
address these imbalances by leveraging transfer learning from pre-trained image
classification models to encoder-based Generative Adversarial Network (eGAN).
To the best of our knowledge, this is the first work to tackle this problem
using GAN without needing to augment with synthesized fake images.
  In the proposed approach we use the discriminator network to output a
negative or positive score. We classify as minority, test samples with negative
scores and as majority those with positive scores. Our approach eliminates
epistemic uncertainty in model predictions, as the P(minority) + P(majority)
need not sum up to 1. The impact of transfer learning and combinations of
different pre-trained image classification models at the generator and
discriminator is also explored. Best result of 0.69 F1-score was obtained on
CIFAR-10 classification task with imbalance ratio of 1:2500.
  Our approach also provides a mechanism of thresholding the specificity or
sensitivity of our machine learning system. Keywords: Class imbalance, Transfer
Learning, GAN, nash equilibrium
