  Generative Adversarial Networks (GANs) have shown remarkable performance in
image synthesis tasks, but typically require a large number of training samples
to achieve high-quality synthesis. This paper proposes a simple and effective
method, Few-Shot GAN (FSGAN), for adapting GANs in few-shot settings (less than
100 images). FSGAN repurposes component analysis techniques and learns to adapt
the singular values of the pre-trained weights while freezing the corresponding
singular vectors. This provides a highly expressive parameter space for
adaptation while constraining changes to the pretrained weights. We validate
our method in a challenging few-shot setting of 5-100 images in the target
domain. We show that our method has significant visual quality gains compared
with existing GAN adaptation methods. We report qualitative and quantitative
results showing the effectiveness of our method. We additionally highlight a
problem for few-shot synthesis in the standard quantitative metric used by
data-efficient image synthesis works. Code and additional results are available
at http://e-271.github.io/few-shot-gan.
