Are all outliers alike? On Understanding the Diversity of Outliers for Detecting OODs

Understand

Deep neural networks (DNNs) are known to produce incorrect predictions with very high confidence on out-of-distribution (OOD) inputs.

• This limitation is one of the key challenges in the adoption of deep learning models in high-assurance systems such as autonomous driving, air traffic management, and medical diagnosis.
• This challenge has received significant attention recently, and several techniques have been developed to detect inputs where the model's prediction cannot be trusted.
• These techniques use different statistical, geometric, or topological signatures.