Overcoming challenges in leveraging GANs for few-shot data augmentation. (arXiv:2203.16662v2 [stat.ML] UPDATED)

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 …

arxiv augmentation challenges data gans ml