  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).
