Long-tailed out-of-distribution detection via normalized outlier distribution adaptation
Onekeychallenge in Out-of-Distribution (OOD) detection is the absence of groundtruth OOD samples during training. One principled approach to address this issue is to use samples from external datasets as outliers (i.e., pseudo OOD samples) to train OOD detectors. However, we find empirically that th...
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| Main Authors: | , , , |
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| 格式: | text |
| 語言: | English |
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Institutional Knowledge at Singapore Management University
2024
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| 在線閱讀: | https://ink.library.smu.edu.sg/sis_research/9877 https://ink.library.smu.edu.sg/context/sis_research/article/10877/viewcontent/10274_Long_Tailed_Out_of_Distr.pdf |
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