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：大量数据的要求是培训许多GAN系统的一个困难。数据高效GAN系统涉及将一台发电机连续的目标分布与一套有限的离散数据样本相匹配,这是一项艰巨的任务。单一图像方法侧重于对单一图像的内部分布进行建模和生成样本。虽然单个图像方法可以以多样性的方式合成图像样本,但它们不能模拟多个图像或捕捉两种图像之间的内在关系。由于只有少量图像,我们有兴趣生成样本并利用输入图像中的共性。在这项工作中,我们扩展了单一图像光谱法以模拟多个图像进行样本合成。我们用一个辅助分类器分支对区分器进行修改,帮助生成多种样本并对输入标签进行分类。我们的数据-有效GAN(DEff-GAN)在输入图像或类别之间能够绘制相似和对应之处时产生极好的结果。</s>