We introduce an inversion based method, denoted as IMAge-Guided model
INvErsion (IMAGINE), to generate high-quality and diverse images from only a
single training sample. We leverage the knowledge of image semantics from a
pre-trained classifier to achieve plausible generations via matching
multi-level feature representations in the classifier, associated with
adversarial training with an external discriminator. IMAGINE enables the
synthesis procedure to simultaneously 1) enforce semantic specificity
constraints during the synthesis, 2) produce realistic images without generator
training, and 3) give users intuitive control over the generation process. With
extensive experimental results, we demonstrate qualitatively and quantitatively
that IMAGINE performs favorably against state-of-the-art GAN-based and
inversion-based methods, across three different image domains (i.e., objects,
scenes, and textures).

介绍了一种基于图像反演的方法 --IMAge-Guided model INvErsion（IMAGINE），通过匹配经过训练的分类器中的多层特征表示和外部鉴别器的对抗训练，来从单一训练样本生成高质量和多样化的图像，实现了在维持语义特性约束的同时生成逼真图像，并且可以让用户直观地控制生成过程。经过大量实验证明，在对象、场景和纹理这三个不同的图像域中，IMAGINE 都比现有的基于 GAN 或基于反演的方法表现更好。