  In order to generate images for a given category, existing deep generative
models generally rely on abundant training images. However, extensive data
acquisition is expensive and fast learning ability from limited data is
necessarily required in real-world applications. Also, these existing methods
are not well-suited for fast adaptation to a new category.
  Few-shot image generation, aiming to generate images from only a few images
for a new category, has attracted some research interest. In this paper, we
propose a Fusing-and-Filling Generative Adversarial Network (F2GAN) to generate
realistic and diverse images for a new category with only a few images. In our
F2GAN, a fusion generator is designed to fuse the high-level features of
conditional images with random interpolation coefficients, and then fills in
attended low-level details with non-local attention module to produce a new
image. Moreover, our discriminator can ensure the diversity of generated images
by a mode seeking loss and an interpolation regression loss. Extensive
experiments on five datasets demonstrate the effectiveness of our proposed
method for few-shot image generation.
