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.

翻译：随着生成模型的发展，生成图像的评估变得越来越重要。以往的方法通过测量参考图像与生成图像在训练视觉模型中特征的距离来进行评估。本文深入探究了生成图像附近表示空间与输入空间之间的关系。我们首先提出了两个与图像中非自然元素存在相关的度量指标：复杂性（表征表示空间的非线性程度）与脆弱性（表征提取特征受对抗性输入变化的易影响程度）。基于此，我们引入了一种新的图像生成模型评估指标——异常分数（Anomaly Score, AS）。此外，我们提出了AS-i（个体图像异常分数），可有效对生成图像进行独立评估。实验结果证明了所提出方法的有效性。