论文标题
不确定性意识深度模型的成功取决于数据流形的几何形状
Success of Uncertainty-Aware Deep Models Depends on Data Manifold Geometry
论文作者
论文摘要
为了在安全 - 关键设置中负责任的决策，机器学习模型必须有效地检测和处理边缘案例数据。尽管现有的作品表明预测不确定性对这些任务有用，但从文献中尚不明显，最不确定性感知模型最适合给定数据集。因此，我们比较了一组边缘任务上的六个不确定性感知的深度学习模型：对对抗性攻击的鲁棒性以及分布外和对抗性检测。我们发现，数据子模型的几何形状是确定各种模型成功的重要因素。我们的发现在研究不确定性意识深度学习模型的研究中提出了一个有趣的方向。
For responsible decision making in safety-critical settings, machine learning models must effectively detect and process edge-case data. Although existing works show that predictive uncertainty is useful for these tasks, it is not evident from literature which uncertainty-aware models are best suited for a given dataset. Thus, we compare six uncertainty-aware deep learning models on a set of edge-case tasks: robustness to adversarial attacks as well as out-of-distribution and adversarial detection. We find that the geometry of the data sub-manifold is an important factor in determining the success of various models. Our finding suggests an interesting direction in the study of uncertainty-aware deep learning models.
