Challenges in leveraging GANs for few-shot data augmentation

  title={Challenges in leveraging GANs for few-shot data augmentation},
  author={Christopher Beckham and Issam H. Laradji and Pau Rodr{\'i}guez L{\'o}pez and David V{\'a}zquez and Derek Nowrouzezahrai and Christopher Joseph Pal},
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… 

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