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Scaling Out-of-Distribution Detection for Real-World Settings
[article]
2022
arXiv
pre-print
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as detecting novel biological phenomena and self-driving cars. However, existing research mainly focuses on simple small-scale settings. To set the stage for more realistic out-of-distribution detection, we depart from small-scale settings and explore large-scale multiclass and multi-label settings with high-resolution images and thousands of classes. To make future work in real-world
arXiv:1911.11132v4
fatcat:6d235jsui5cjbciufvbxn65vcy