  Learning to generate new images for a novel category based on only a few
images, named as few-shot image generation, has attracted increasing research
interest. Several state-of-the-art works have yielded impressive results, but
the diversity is still limited. In this work, we propose a novel Delta
Generative Adversarial Network (DeltaGAN), which consists of a reconstruction
subnetwork and a generation subnetwork. The reconstruction subnetwork captures
intra-category transformation, i.e., delta, between same-category pairs. The
generation subnetwork generates sample-specific delta for an input image, which
is combined with this input image to generate a new image within the same
category. Besides, an adversarial delta matching loss is designed to link the
above two subnetworks together. Extensive experiments on six benchmark datasets
demonstrate the effectiveness of our proposed method. Our code is available at
https://github.com/bcmi/DeltaGAN-Few-Shot-Image-Generation.
