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 with limited real samples. In this work, we present 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 more diverse images which share partially common features with training samples as realistic images. Correspondingly, the generator is guided to generate more diverse images instead of replicating training samples. In addition, we employ cross-domain consistency loss for the discriminator to keep relative distances between samples in its feature space. The discriminator cross-domain consistency loss serves as another optimization target in addition to adversarial loss 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:少量图像生成旨在生成高质量和多样性高且数据有限的图像。然而,现代GAN很难避免在仅用少量图像进行训练时过度装配。歧视者可以很容易地记住所有培训样本,并指导生成者复制这些样本,从而导致严重多样性退化。建议采取几种方法,通过改造大源域预先培训的GANs,将大源域与实际样本有限的领域相匹配,来缓解过度装配过度装配。在这项工作中,我们提出了一个新颖的方法,通过掩蔽歧视实现少发GAN适应性。随机面罩用于歧视者从输入图像中提取的特征。我们的目标是鼓励歧视者判断与培训样本作为现实图像具有部分共同特征的更多样化图像。相应的是,引导生成者制作更多样化的图像,而不是复制培训样本。此外,我们采用交叉的一致性损失使歧视者在其特征空间保持相对距离。除了对等损失之外,还作为另一种优化目标,并指导调整GANs对调出的图像从高源域中获取更多信息,以保持高质量和更高质量。前图像生成方法的有效性。