  The adaptation of a Generative Adversarial Network (GAN) aims to transfer a
pre-trained GAN to a target domain with limited training data. In this paper,
we focus on the one-shot case, which is more challenging and rarely explored in
previous works. We consider that the adaptation from a source domain to a
target domain can be decoupled into two parts: the transfer of global style
like texture and color, and the emergence of new entities that do not belong to
the source domain. While previous works mainly focus on style transfer, we
propose a novel and concise framework to address the \textit{generalized
one-shot adaptation} task for both style and entity transfer, in which a
reference image and its binary entity mask are provided. Our core idea is to
constrain the gap between the internal distributions of the reference and
syntheses by sliced Wasserstein distance. To better achieve it, style fixation
is used at first to roughly obtain the exemplary style, and an auxiliary
network is introduced to the generator to disentangle entity and style
transfer. Besides, to realize cross-domain correspondence, we propose the
variational Laplacian regularization to constrain the smoothness of the adapted
generator. Both quantitative and qualitative experiments demonstrate the
effectiveness of our method in various scenarios. Code is available at
\url{https://github.com/zhangzc21/Generalized-One-shot-GAN-adaptation}.
