Out-of-distribution Detection with Implicit Outlier Transformation

Understand

Outlier exposure (OE) is powerful in out-of-distribution (OOD) detection, enhancing detection capability via model fine-tuning with surrogate OOD data.

• However, surrogate data typically deviate from test OOD data.
• Thus, the performance of OE, when facing unseen OOD data, can be weakened.
• To address this issue, we propose a novel OE-based approach that makes the model perform well for unseen OOD situations, even for unseen OOD cases.