Smoothness Similarity Regularization for Few-Shot GAN Adaptation
Vadim Sushko Ruyu Wang Juergen Gall
The task of few-shot GAN adaptation aims to adapt a pre-trained GAN model to
a small dataset with very few training images. While existing methods perform
well when the dataset for pre-training is structurally similar to the target
dataset, the approaches suffer from training instabilities or memorization
issues when the objects in the two domains have a very different structure. To
mitigate this limitation, we propose a new smoothness similarity regularization
that transfers the inherently learned smoothness of the pre-trained GAN to the
few-shot target domain even if the two domains are very different. We evaluate
our approach by adapting an unconditional and a class-conditional GAN to
diverse few-shot target domains. Our proposed method significantly outperforms
prior few-shot GAN adaptation methods in the challenging case of structurally
dissimilar source-target domains, while performing on par with the state of the
art for similar source-target domains.