Few-shot image generation aims to generate images of high quality and great diversity with limited data. However, it is difficult for modern GANs to avoid overfitting when trained on only a few images. The discriminator can easily remember all the training samples and guide the generator to replicate them, leading to severe diversity degradation. Several methods have been proposed to relieve overfitting by adapting GANs pre-trained on large source domains to target domains using limited real samples. This work presents a novel approach to realize few-shot GAN adaptation via masked discrimination. Random masks are applied to features extracted by the discriminator from input images. We aim to encourage the discriminator to judge various images which share partially common features with training samples as realistic. Correspondingly, the generator is guided to generate diverse images instead of replicating training samples. In addition, we employ a cross-domain consistency loss for the discriminator to keep relative distances between generated samples in its feature space. It strengthens global image discrimination and guides adapted GANs to preserve more information learned from source domains for higher image quality. The effectiveness of our approach is demonstrated both qualitatively and quantitatively with higher quality and greater diversity on a series of few-shot image generation tasks than prior methods.

翻译：少样本图像生成旨在利用有限的数据生成高质量且多样化的图像。然而，当仅基于少量图像训练时，现代生成对抗网络难以避免过拟合问题。判别器容易记住所有训练样本，并引导生成器复制它们，从而导致严重的多样性退化。已有多种方法通过将预训练于大型源域的生成对抗网络适配到目标域（仅使用少量真实样本）来缓解过拟合问题。本文提出了一种通过掩码判别实现少样本生成对抗网络适配的新方法。我们对判别器从输入图像中提取的特征施加随机掩码，旨在促使判别器将那些与训练样本共享部分共同特征的多种图像判定为真实图像。相应地，生成器被引导生成多样化的图像，而非复制训练样本。此外，我们为判别器引入跨域一致性损失，以保持生成样本在其特征空间中的相对距离。这强化了全局图像判别能力，并引导适配后的生成对抗网络保留更多从源域学到的信息，从而获得更高的图像质量。通过一系列少样本图像生成任务的定性与定量评估，本方法在生成质量和多样性方面均优于现有方法。