论文标题
完美密度模型无法保证检测异常
Perfect density models cannot guarantee anomaly detection
论文作者
论文摘要
由于其可能性的障碍，几种深层生成模型对看似直接但重要的应用（例如异常检测，不确定性估计和积极学习）表现出了希望。但是，凭经验上的可能性值归因于异常与这些提议的应用所表明的期望相冲突。在本文中，我们通过修复镜头仔细研究了分配密度的行为，并表明这些数量的有意义的信息比以前想象的要少，超出了估计问题或维数的诅咒。我们得出的结论是，将这些可能性用于异常检测的使用依赖于强烈和隐性的假设，并强调了明确制定这些假设以进行可靠的异常检测的必要性。
Thanks to the tractability of their likelihood, several deep generative models show promise for seemingly straightforward but important applications like anomaly detection, uncertainty estimation, and active learning. However, the likelihood values empirically attributed to anomalies conflict with the expectations these proposed applications suggest. In this paper, we take a closer look at the behavior of distribution densities through the lens of reparametrization and show that these quantities carry less meaningful information than previously thought, beyond estimation issues or the curse of dimensionality. We conclude that the use of these likelihoods for anomaly detection relies on strong and implicit hypotheses, and highlight the necessity of explicitly formulating these assumptions for reliable anomaly detection.
