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

Understand

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 settings possible, we create new benchmarks for three large-scale settings.