Introducing 'Inside' Out of Distribution

TL;DR

This paper introduces a new perspective on out-of-distribution detection by distinguishing between inside and outside OOD, analyzing their effects on model performance using synthetic datasets and performance metrics.

Contribution

It proposes the division of OOD into inside and outside categories and analyzes their distinct impacts on machine learning models, which is a novel approach.

Findings

Outside OOD causes greater performance degradation than inside OOD.

Different inside-outside OOD profiles lead to unique effects on model performance.

Highlighting the importance of distinguishing between inside and outside OOD for better model robustness.

Abstract

Detecting and understanding out-of-distribution (OOD) samples is crucial in machine learning (ML) to ensure reliable model performance. Current OOD studies primarily focus on extrapolatory (outside) OOD, neglecting potential cases of interpolatory (inside) OOD. In this study, we introduce a novel perspective on OOD by suggesting it can be divided into inside and outside cases. We examine the inside-outside OOD profiles of datasets and their impact on ML model performance, using normalized Root Mean Squared Error (RMSE) and F1 score as the performance metrics on syntetically-generated datasets with both inside and outside OOD. Our analysis demonstrates that different inside-outside OOD profiles lead to unique effects on ML model performance, with outside OOD generally causing greater performance degradation, on average. These findings highlight the importance of distinguishing between inside and outside OOD for developing effective counter-OOD methods.