  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 five few-shot image
datasets demonstrate the effectiveness of our proposed method.
