论文标题
通过对抗生成异常值评估分布探测器
Evaluating Out-of-Distribution Detectors Through Adversarial Generation of Outliers
论文作者
论文摘要
可靠的评估方法对于建立强大的分布（OOD）检测器至关重要。 OOD检测器的当前鲁棒性评估协议依赖于向数据注射扰动。但是，扰动不太可能自然发生或与数据内容无关，从而提供了有限的鲁棒性评估。在本文中，我们提出了OOD检测器（EVG）的评估-VIA产生，这是一种新的协议，用于研究异常值变化模式下OOD检测器的鲁棒性。 EVG利用生成模型合成合理的异常值，并采用MCMC采样来发现离群值错误地分类为探测器的最高置信度。我们使用EVG对最先进的OOD检测器的性能进行了全面的基准比较，从而揭示了先前被忽视的弱点。
A reliable evaluation method is essential for building a robust out-of-distribution (OOD) detector. Current robustness evaluation protocols for OOD detectors rely on injecting perturbations to outlier data. However, the perturbations are unlikely to occur naturally or not relevant to the content of data, providing a limited assessment of robustness. In this paper, we propose Evaluation-via-Generation for OOD detectors (EvG), a new protocol for investigating the robustness of OOD detectors under more realistic modes of variation in outliers. EvG utilizes a generative model to synthesize plausible outliers, and employs MCMC sampling to find outliers misclassified as in-distribution with the highest confidence by a detector. We perform a comprehensive benchmark comparison of the performance of state-of-the-art OOD detectors using EvG, uncovering previously overlooked weaknesses.
