  One-shot fine-grained visual recognition often suffers from the problem of
having few training examples for new fine-grained classes. To alleviate this
problem, off-the-shelf image generation techniques based on Generative
Adversarial Networks (GANs) can potentially create additional training images.
However, these GAN-generated images are often not helpful for actually
improving the accuracy of one-shot fine-grained recognition. In this paper, we
propose a meta-learning framework to combine generated images with original
images, so that the resulting "hybrid" training images improve one-shot
learning. Specifically, the generic image generator is updated by a few
training instances of novel classes, and a Meta Image Reinforcing Network
(MetaIRNet) is proposed to conduct one-shot fine-grained recognition as well as
image reinforcement. Our experiments demonstrate consistent improvement over
baselines on one-shot fine-grained image classification benchmarks.
Furthermore, our analysis shows that the reinforced images have more diversity
compared to the original and GAN-generated images.
