  When presented with a binary classification problem where the data exhibits
severe class imbalance, most standard predictive methods may fail to accurately
model the minority class. We present a model based on Generative Adversarial
Networks which uses additional regularization losses to map majority samples to
corresponding synthetic minority samples. This translation mechanism encourages
the synthesized samples to be close to the class boundary. Furthermore, we
explore a selection criterion to retain the most useful of the synthesized
samples. Experimental results using several downstream classifiers on a variety
of tabular class-imbalanced datasets show that the proposed method improves
average precision when compared to alternative re-weighting and oversampling
techniques.
