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

Jaehui Hwang, Junghyuk Lee, Jong-Seok Lee; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2024, pp. 8754-8763

With the advancement of generative models the assessment of generated images becomes increasingly 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.