论文标题
最大似然不确定性估计：异常值的鲁棒性
Maximum Likelihood Uncertainty Estimation: Robustness to Outliers
论文作者
论文摘要
我们基于对回归任务的培训数据中的离群值的最大不确定性估计方法的鲁棒性。培训数据中的离群值或嘈杂标签导致性能降解以及不确定性的错误估计。我们建议使用重尾分布（拉普拉斯分布）来改善异常值的鲁棒性。使用标准回归基准和单眼深度估计的高维回归任务评估该属性，均包含异常值。特别是，基于重尾分布的最大似然可提供更好的不确定性估计值，在异常值存在下，在分布外数据的不确定性中更好地分开了对抗性攻击。
We benchmark the robustness of maximum likelihood based uncertainty estimation methods to outliers in training data for regression tasks. Outliers or noisy labels in training data results in degraded performances as well as incorrect estimation of uncertainty. We propose the use of a heavy-tailed distribution (Laplace distribution) to improve the robustness to outliers. This property is evaluated using standard regression benchmarks and on a high-dimensional regression task of monocular depth estimation, both containing outliers. In particular, heavy-tailed distribution based maximum likelihood provides better uncertainty estimates, better separation in uncertainty for out-of-distribution data, as well as better detection of adversarial attacks in the presence of outliers.
