With the advancement of generative models, the assessment of generated images
becomes more and more important. Previous methods measure distances between
features of reference and generated images from trained vision models. In this
paper, we conduct an extensive investigation into the relationship between the
representation space and input space around generated images. We first propose
two measures related to the presence of unnatural elements within images:
complexity, which indicates how non-linear the representation space is, and
vulnerability, which is related to how easily the extracted feature changes by
adversarial input changes. Based on these, we introduce a new metric to
evaluating image-generative models called anomaly score (AS). Moreover, we
propose AS-i (anomaly score for individual images) that can effectively
evaluate generated images individually. Experimental results demonstrate the
validity of the proposed approach.

基于生成模型的评估，该文研究了生成图像的表示空间与输入空间之间的关系，并提出了一种新的评估方法 —— 异常分数（AS）和个体图像的异常分数（AS-i），实验结果证明了该方法的有效性。