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Perfect density models cannot guarantee anomaly detection
[article]
2021
arXiv
pre-print
Thanks to the tractability of their likelihood, some 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 and show that these quantities carry less meaningful information than
arXiv:2012.03808v2
fatcat:kchdoxof6bhbxm6k3cnbopjuum