  We propose a Paired Few-shot GAN (PFS-GAN) model for learning generators with
sufficient source data and a few target data. While generative model learning
typically needs large-scale training data, our PFS-GAN not only uses the
concept of few-shot learning but also domain shift to transfer the knowledge
across domains, which alleviates the issue of obtaining low-quality generator
when only trained with target domain data. The cross-domain datasets are
assumed to have two properties: (1) each target-domain sample has its
source-domain correspondence and (2) two domains share similar content
information but different appearance. Our PFS-GAN aims to learn the
disentangled representation from images, which composed of domain-invariant
content features and domain-specific appearance features. Furthermore, a
relation loss is introduced on the content features while shifting the
appearance features to increase the structural diversity. Extensive experiments
show that our method has better quantitative and qualitative results on the
generated target-domain data with higher diversity in comparison to several
baselines.

Computer Vision and Pattern Recognition