Overcoming challenges in leveraging GANs for few-shot data augmentation

Abstract

In this paper, we explore the use of GAN-based few-shot data augmentation as a method to improve few-shot classification performance. We perform exploration into how a GAN can be fine-tuned for such a task (one of which is in a class-incremental manner), as well as a rigorous empirical investigation into how well these models can perform to improve the few-shot classification. We identify issues related to the difficulty of training such generative models under a purely supervised regime with very few examples, as well as issues regarding the evaluation protocols of existing works. We also find that in this regime, classification accuracy is highly sensitive to how the classes of the dataset are randomly split. Therefore, a semi-supervised approach may be needed to achieve strong empirical gains.