Corpus ID: 227407829

Scaling Out-of-Distribution Detection for Real-World Settings

@article{Hendrycks2020ScalingOD,
  title={Scaling Out-of-Distribution Detection for Real-World Settings},
  author={Dan Hendrycks and Steven Basart and Mantas Mazeika and Mohammadreza Mostajabi and J. Steinhardt and D. Song},
  journal={arXiv: Computer Vision and Pattern Recognition},
  year={2020}
}
Detecting out-of-distribution examples is important for safety-critical machine learning applications such as medical screening 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 hundreds of classes. To make future work in real-world settings possible, we… Expand
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