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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| المؤلفون الرئيسيون: | , , , |
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| التنسيق: | text |
| اللغة: | English |
| منشور في: |
Institutional Knowledge at Singapore Management University
2024
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| الموضوعات: | |
| الوصول للمادة أونلاين: | 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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