论文标题
Deltagan：使用特定的三角
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific Delta
论文作者
论文摘要
学习为仅基于几个图像（称为少数图像产生的少数图像）生成新类别的新图像，引起了研究的兴趣。几项最先进的作品取得了令人印象深刻的结果，但多样性仍然有限。在这项工作中，我们提出了一个新颖的Delta生成对抗网络（Deltagan），该网络由重建子网和一代子网组成。重建子网捕获了类别内转换，即同一类别对之间的三角洲。该生成子网为输入图像生成了特定于样本的三角洲，该图像与此输入图像结合使用，以在同一类别中生成新图像。此外，对抗性的三角洲匹配损失旨在将上述两个子网链接在一起。六个基准数据集的广泛实验证明了我们提出的方法的有效性。我们的代码可从https://github.com/bcmi/deltagan-few-shot-image-generation获得。
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.
