How Does Unlabeled Data Provably Help Out-of-Distribution Detection?

Understand

Using unlabeled data to regularize the machine learning models has demonstrated promise for improving safety and reliability in detecting out-of-distribution (OOD) data.

• Harnessing the power of unlabeled in-the-wild data is non-trivial due to the heterogeneity of both in-distribution (ID) and OOD data.
• This lack of a clean set of OOD samples poses significant challenges in learning an optimal OOD classifier.
• Currently, there is a lack of research on formally understanding how unlabeled data helps OOD detection.