Improving Few-shot Image Generation by Structural Discrimination and
  Textural Modulation
Mengping Yang Zhe Wang Wenyi Feng Qian Zhang Ting Xiao
Few-shot image generation, which aims to produce plausible and diverse images
for one category given a few images from this category, has drawn extensive
attention. Existing approaches either globally interpolate different images or
fuse local representations with pre-defined coefficients. However, such an
intuitive combination of images/features only exploits the most relevant
information for generation, leading to poor diversity and coarse-grained
semantic fusion. To remedy this, this paper proposes a novel textural
modulation (TexMod) mechanism to inject external semantic signals into internal
local representations. Parameterized by the feedback from the discriminator,
our TexMod enables more fined-grained semantic injection while maintaining the
synthesis fidelity. Moreover, a global structural discriminator (StructD) is
developed to explicitly guide the model to generate images with reasonable
layout and outline. Furthermore, the frequency awareness of the model is
reinforced by encouraging the model to distinguish frequency signals. Together
with these techniques, we build a novel and effective model for few-shot image
generation. The effectiveness of our model is identified by extensive
experiments on three popular datasets and various settings. Besides achieving
state-of-the-art synthesis performance on these datasets, our proposed
techniques could be seamlessly integrated into existing models for a further
performance boost.