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 language | English |
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Title of host publication | Proceedings - 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 |
Publisher | IEEE Computer Society |
Pages | 8754-8763 |
Number of pages | 10 |
ISBN (Electronic) | 9798350353006 |
DOIs | |
Publication status | Published - 2024 |
Event | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 - Seattle, United States Duration: 2024 Jun 16 → 2024 Jun 22 |
Publication series
Name | Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition |
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ISSN (Print) | 1063-6919 |
Conference
Conference | 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2024 |
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Country/Territory | United States |
City | Seattle |
Period | 24/6/16 → 24/6/22 |
Bibliographical note
Publisher Copyright:© 2024 IEEE.
All Science Journal Classification (ASJC) codes
- Software
- Computer Vision and Pattern Recognition