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.

该论文提出了一种基于 Delta GAN 的少样本图像生成方法，其中的重构子网络和生成子网络一起将输入图像变换为同类别的新图像，并配合对抗性 delta 匹配损失函数使其更加多样化。此方法在五种不同的少样本图像数据集上进行了实验验证其有效性。

DeltaGAN: 基于样本特异性的多样化少样本图像生成