In few-shot image generation, directly training GAN models on just a handful of images faces the risk of overfitting. A popular solution is to transfer the models pretrained on large source domains to small target ones. In this work, we introduce WeditGAN, which realizes model transfer by editing the intermediate latent codes $w$ in StyleGANs with learned constant offsets ($\Delta w$), discovering and constructing target latent spaces via simply relocating the distribution of source latent spaces. The established one-to-one mapping between latent spaces can naturally prevents mode collapse and overfitting. Besides, we also propose variants of WeditGAN to further enhance the relocation process by regularizing the direction or finetuning the intensity of $\Delta w$. Experiments on a collection of widely used source/target datasets manifest the capability of WeditGAN in generating realistic and diverse images, which is simple yet highly effective in the research area of few-shot image generation.

本文介绍了一种基于WeditGAN的few-shot image generation方法，利用常数偏移量编辑StyleGANs中的中间潜在层代码w，通过重新定位源潜在空间的分布，构建目标潜在空间，成功地实现了模型转移，并在多个数据集上进行了实验以证明其在few-shot image generation方面的高效性。

WeditGAN: 通过潜空间重新定位进行少样本图像生成