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.

翻译：少样本GAN适配任务旨在将预训练的GAN模型适配到包含极少训练图像的小数据集上。现有方法在预训练数据集与目标数据集结构相似时表现良好，但当两个域中的对象结构差异较大时，这些方法会出现训练不稳定或记忆化问题。为缓解这一局限，我们提出了一种新的平滑相似性正则化方法，该方法能够将预训练GAN内隐学习的平滑性迁移到少样本目标域，即使两个域差异显著。我们通过将无条件GAN和类条件GAN适配到不同的少样本目标域来评估该方法。在源-目标域结构差异显著的挑战性场景中，我们的方法显著优于先前的少样本GAN适配方法，同时在源-目标域结构相似的情况下与现有最先进技术性能相当。