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With the advancement of generative models, the assessment of generated imagesbecomes more and more important. Previous methods measure distances betweenfeatures of reference and generated images from trained vision models. In thispaper, we conduct an extensive investigation into the relationship between therepresentation space and input space around generated images. We first proposetwo measures related to the presence of unnatural elements within images:complexity, which indicates how non-linear the representation space is, andvulnerability, which is related to how easily the extracted feature changes byadversarial input changes. Based on these, we introduce a new metric toevaluating image-generative models called anomaly score (AS). Moreover, wepropose AS-i (anomaly score for individual images) that can effectivelyevaluate generated images individually. Experimental results demonstrate thevalidity of the proposed approach.

Anomaly Score: Evaluating Generative Models and Individual Generated Images based on Complexity and Vulnerability