  Generative Adversarial Networks (GAN) boast impressive capacity to generate
realistic images. However, like much of the field of deep learning, they
require an inordinate amount of data to produce results, thereby limiting their
usefulness in generating novelty. In the same vein, recent advances in
meta-learning have opened the door to many few-shot learning applications. In
the present work, we propose Few-shot Image Generation using Reptile (FIGR), a
GAN meta-trained with Reptile. Our model successfully generates novel images on
both MNIST and Omniglot with as little as 4 images from an unseen class. We
further contribute FIGR-8, a new dataset for few-shot image generation, which
contains 1,548,944 icons categorized in over 18,409 classes. Trained on FIGR-8,
initial results show that our model can generalize to more advanced concepts
(such as "bird" and "knife") from as few as 8 samples from a previously unseen
class of images and as little as 10 training steps through those 8 images. This
work demonstrates the potential of training a GAN for few-shot image generation
and aims to set a new benchmark for future work in the domain.
