Discriminative feature generation for classification of imbalanced data

•A novel supervised discriminative feature generation (DFG) method using attention maps in the feature space is presented.•We combine transfer learning and adversarial feature augmentation to complement their drawbacks.•Extensive experiments on various datasets show that the proposed method enhances the augmentation of label-preserved and diverse features, and the classification results are significantly improved.