  Assessing the performance of Generative Adversarial Networks (GANs) has been
an important topic due to its practical significance. Although several
evaluation metrics have been proposed, they generally assess the quality of the
whole generated image distribution. For Reference-guided Image Synthesis (RIS)
tasks, i.e., rendering a source image in the style of another reference image,
where assessing the quality of a single generated image is crucial, these
metrics are not applicable. In this paper, we propose a general learning-based
framework, Reference-guided Image Synthesis Assessment (RISA) to quantitatively
evaluate the quality of a single generated image. Notably, the training of RISA
does not require human annotations. In specific, the training data for RISA are
acquired by the intermediate models from the training procedure in RIS, and
weakly annotated by the number of models' iterations, based on the positive
correlation between image quality and iterations. As this annotation is too
coarse as a supervision signal, we introduce two techniques: 1) a pixel-wise
interpolation scheme to refine the coarse labels, and 2) multiple binary
classifiers to replace a na\"ive regressor. In addition, an unsupervised
contrastive loss is introduced to effectively capture the style similarity
between a generated image and its reference image. Empirical results on various
datasets demonstrate that RISA is highly consistent with human preference and
transfers well across models.
