Requirements of large amounts of data is a difficulty in training many GANs. Data efficient GANs involve fitting a generators continuous target distribution with a limited discrete set of data samples, which is a difficult task. Single image methods have focused on modeling the internal distribution of a single image and generating its samples. While single image methods can synthesize image samples with diversity, they do not model multiple images or capture the inherent relationship possible between two images. Given only a handful of images, we are interested in generating samples and exploiting the commonalities in the input images. In this work, we extend the single-image GAN method to model multiple images for sample synthesis. We modify the discriminator with an auxiliary classifier branch, which helps to generate a wide variety of samples and to classify the input labels. Our Data-Efficient GAN (DEff-GAN) generates excellent results when similarities and correspondences can be drawn between the input images or classes.

翻译：大量数据需求是训练许多生成对抗网络（GAN）时面临的难点。数据高效型GAN需要利用有限的离散数据样本拟合生成器的连续目标分布，这是一项艰巨任务。单图像方法专注于建模单张图像的内部分布并生成其样本。尽管单图像方法能合成具有多样性的图像样本，但无法建模多张图像，也无法捕捉两幅图像之间可能存在的固有关系。我们旨在仅凭少量图像生成样本并挖掘输入图像中的共性特征。本研究将单图像GAN方法扩展至多图像建模以进行样本合成。我们通过辅助分类器分支改进判别器，该方法既能生成多样化的样本，又能对输入标签进行分类。当输入图像或类别之间存在相似性与对应关系时，我们的数据高效型GAN（DEff-GAN）能生成优异的结果。