Improving out-of-distribution detection with disentangled foreground and background features

Detecting out-of-distribution (OOD) inputs is a principal task for ensuring the safety of deploying deep-neural-network classifiers in open-set scenarios. OOD samples can be drawn from arbitrary distributions and exhibit deviations from in-distribution (ID) data in various dimensions, such as foregr...

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Bibliographic Details
Main Authors: DING, Choubo, PANG, Guansong
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2024
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Online Access:https://ink.library.smu.edu.sg/sis_research/9756
https://ink.library.smu.edu.sg/context/sis_research/article/10756/viewcontent/2303.08727v2.pdf
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Institution: Singapore Management University
Language: English
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