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

Jaehui Hwang, Junghyuk Lee, Jong Seok Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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 nonlinear 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.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
PublisherIEEE Computer Society
Pages8754-8763
Number of pages10
ISBN (Electronic)9798350353006
DOIs
Publication statusPublished - 2024
Event2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States
Duration: 2024 Jun 162024 Jun 22

Publication series

NameProceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
ISSN (Print)1063-6919

Conference

Conference2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024
Country/TerritoryUnited States
CitySeattle
Period24/6/1624/6/22

Bibliographical note

Publisher Copyright:
© 2024 IEEE.

All Science Journal Classification (ASJC) codes

  • Software
  • Computer Vision and Pattern Recognition

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