Mitigating distributional shift in semantic segmentation via uncertainty estimation from unlabeled data
Knowing when a trained segmentation model is encountering data that is different to its training data is important. Understanding and mitigating the effects of this play an important part in their application from a performance and assurance perspective—this being a safety concern in applications su...
| Hlavní autoři: | , , , |
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| Médium: | Journal article |
| Jazyk: | English |
| Vydáno: |
IEEE
2024
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