DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
This work addresses the challenge of generating diverse images from few examples for applications in computer vision, representing an incremental improvement over existing methods.
The paper tackles the problem of limited diversity in few-shot image generation by proposing DeltaGAN, which uses sample-specific delta transformations to generate new images within a category, achieving improved diversity as demonstrated on six benchmark datasets.
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.