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Abstract
While there has been a growing research interest in developing out-of-distribution (OOD) detection methods, there has been comparably little discussion around how these methods should be evaluated. Given their relevance for safe(r) AI, it is important to examine whether the basis for comparing OOD detection methods is consistent with practical needs. In this work, we take a closer look at the go-to metrics for evaluating OOD detection, and question the approach of exclusively reducing OOD detection to a binary classification task with little consideration for the detection threshold. We illustrate the limitations of current metrics (AUROC & its friends) and propose a new metric - Area Under the Threshold Curve (AUTC), which explicitly penalizes poor separation between ID and OOD samples. Scripts and data are available at https://github.com/glhr/beyond-auroc
Original language | English |
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Title of host publication | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
Number of pages | 10 |
Publisher | IEEE (Institute of Electrical and Electronics Engineers) |
Publication date | Aug 2023 |
Pages | 3881-3890 |
ISBN (Electronic) | 979-8-3503-0249-3 |
DOIs | |
Publication status | Published - Aug 2023 |
Event | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 - Vancouver, Canada Duration: 18 Jun 2023 → 22 Jun 2023 |
Conference
Conference | 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023 |
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Country/Territory | Canada |
City | Vancouver |
Period | 18/06/2023 → 22/06/2023 |
Series | IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) |
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ISSN | 2160-7516 |
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"Are you sure?" Towards trustworthy computer vision
Humblot-Renaux, G. (PI), Guerrero, S. E. (Supervisor) & Moeslund, T. B. (Supervisor)
01/10/2022 → 01/10/2025
Project: PhD Project