Understanding the relationships between data points in the latent decision space derived by the deep learning system is critical to evaluating and interpreting the performance of the system on real world data. Detecting \textit{out-of-distribution} (OOD) data for deep learning systems continues to be an active research topic. We investigate the connection between latent space OOD detection and classification accuracy of the model. Using open source simulated and measured Synthetic Aperture RADAR (SAR) datasets, we empirically demonstrate that the OOD detection cannot be used as a proxy measure for model performance. We hope to inspire additional research into the geometric properties of the latent space that may yield future insights into deep learning robustness and generalizability.

翻译：理解深度学习系统所构建的潜在决策空间中数据点之间的关系，对于评估和解释该系统在真实世界数据上的性能至关重要。针对深度学习系统的分布外数据检测目前仍是活跃的研究课题。本文探究了潜在空间中的分布外数据检测与模型分类精度之间的关联。通过使用开源模拟数据与实测合成孔径雷达数据集，我们实证表明分布外数据检测不能作为模型性能的替代度量指标。我们希望此项研究能激发更多关于潜在空间几何特性的探索，这些特性可能为深度学习模型的鲁棒性与泛化能力提供新的理论洞见。