  The data imbalance problem is a frequent bottleneck in the classification
performance of neural networks. In this paper, we propose a novel supervised
discriminative feature generation (DFG) method for a minority class dataset.
DFG is based on the modified structure of a generative adversarial network
consisting of four independent networks: generator, discriminator, feature
extractor, and classifier. To augment the selected discriminative features of
the minority class data by adopting an attention mechanism, the generator for
the class-imbalanced target task is trained, and the feature extractor and
classifier are regularized using the pre-trained features from a large source
data. The experimental results show that the DFG generator enhances the
augmentation of the label-preserved and diverse features, and the
classification results are significantly improved on the target task. The
feature generation model can contribute greatly to the development of data
augmentation methods through discriminative feature generation and supervised
attention methods.
