  Detecting and understanding out-of-distribution (OOD) samples is crucial in
machine learning (ML) to ensure reliable model performance. Current OOD
studies, in general, and in the context of ML, in particular, primarily focus
on extrapolatory OOD (outside), neglecting potential cases of interpolatory OOD
(inside). This study introduces a novel perspective on OOD by suggesting OOD
can be divided into inside and outside cases. In addition, following this
framework, we examine the inside-outside OOD profiles of datasets and their
impact on ML model performance. Our analysis shows that different
inside-outside OOD profiles lead to nuanced declines in ML model performance,
highlighting the importance of distinguishing between these two cases for
developing effective counter-OOD methods.
