  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.

Computer Vision and Pattern Recognition