Title: Introducing 'Inside' Out of Distribution Show Chinese title

Title: 引入“内部”分布外

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. Show Chinese abstract

Abstract: 检测和理解分布外（OOD）样本在机器学习（ML）中至关重要，以确保模型的可靠性能。 当前的OOD研究主要集中在外部OOD上，忽视了内部OOD的可能性。 在本研究中，我们通过提出OOD可以分为内部和外部情况，引入了一个新的视角。 我们检查了数据集的内部-外部OOD特征及其对ML模型性能的影响，使用归一化均方根误差（RMSE）和F1分数作为在同时包含内部和外部OOD的合成数据集上的性能指标。 我们的分析表明，不同的内部-外部OOD特征会对ML模型性能产生独特的影响，平均而言，外部OOD通常导致更大的性能下降。 这些发现强调了区分内部和外部OOD对于开发有效的抗OOD方法的重要性。