Title: PGASL: Predictive and Generative Adversarial Semi-supervised Learning for imbalanced data. Abstract: Modern machine learning techniques often suffer from class imbalance where only a small amount of data is available for minority classes. Classifiers trained on an imbalanced dataset, although have high accuracy on majority classes, can perform poorly on minority classes. This is problematic when minority classes are also important. Generative Adversarial Networks (GANs) have been proposed for generating artificial minority examples to balance the training. We propose a class-imbalanced semi-supervised learning algorithm PGASL which can be efficiently trained on unlabeled and class-imbalanced data. In this work, we use a predictive network which is trained adversarially for the discriminator to correct predictions on the unlabeled dataset. Experiments on text datasets show that PGASL outperforms state-of-the-art class-imbalanced learning algorithms by including both predictive network and generator. 
