深目标探测器回归预测不确定度的估计与评价
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
摘要：
预测性不确定性估计是在安全关键任务中可靠部署深部物体探测器的重要下一步。在这项工作中，我们专注于估计预测分布的限界框回归输出方差网络。我们表明，在目标检测的背景下，负对数似然（NLL）的训练方差网络可以导致高熵预测分布，无论输出均值的正确性如何。我们建议使用能量分数作为非局部适当的评分规则，并发现当用于训练时，能量分数导致比NLL更好的校准和更低的熵预测分布。我们还解决了广泛使用的非适当的评分指标，以评估预测分布的深度对象检测器提出了一种替代的评估方法，建立在适当的评分规则。使用建议的评估工具，我们表明，虽然方差网络可以用来产生高质量的预测分布，ad-hoc方法用于种子对象检测器在训练过程中选择回归目标不提供足够广泛的数据支持可靠的方差学习。我们希望我们的工作有助于转移概率目标检测中的评估，以更好地与其他机器学习领域中的预测不确定性评估保持一致。所有模型、评估和数据集的代码可在www.example.com上获得。
英文摘要：
Predictive uncertainty estimation is an essential next step for the reliable deployment of deep object detectors in safety-critical tasks. In this work, we focus on estimating predictive distributions for bounding box regression output with variance networks. We show that in the context of object detection, training variance networks with negative log likelihood (NLL) can lead to high entropy predictive distributions regardless of the correctness of the output mean. We propose to use the energy score as a non-local proper scoring rule and find that when used for training, the energy score leads to better calibrated and lower entropy predictive distributions than NLL. We also address the widespread use of non-proper scoring metrics for evaluating predictive distributions from deep object detectors by proposing an alternate evaluation approach founded on proper scoring rules. Using the proposed evaluation tools, we show that although variance networks can be used to produce high quality predictive distributions, ad-hoc approaches used by seminal object detectors for choosing regression targets during training do not provide wide enough data support for reliable variance learning. We hope that our work helps shift evaluation in probabilistic object detection to better align with predictive uncertainty evaluation in other machine learning domains. Code for all models, evaluation, and datasets is available at: https://github.com/asharakeh/probdet.git.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
