  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 an
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 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, we propose a semi-supervised fine-tuning
approach as a more pragmatic way forward to address these problems.
