分析深度模型隐空间，发现异常数据检测不能代表模型性能。
Latent space analysis and generalization to out-of-distribution data
- 通过实证研究隐空间中数据分布与模型表现的关系。
- 在合成雷达数据上验证异常检测无法反映分类准确率。
- 揭示隐空间几何特性对模型鲁棒性的重要意义。
理解深度学习系统生成的隐决策空间中数据点之间的关系，对于评估和解释模型在真实数据上的表现至关重要。针对深度学习系统的外分布（OOD）数据检测仍是活跃研究课题。本文通过开源的模拟与实测合成孔径雷达（SAR）数据集，实证表明：隐空间中的OOD检测不能作为模型性能的代理指标。研究旨在激发对隐空间几何性质的进一步探索，以期为深度学习的鲁棒性与泛化能力提供新见解。
原文摘要 · Abstract (English)
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.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。