Further Analysis of Outlier Detection with Deep Generative Models

• URL: http://arxiv.org/abs/2010.13064v1
• Date: Sun, 25 Oct 2020 08:20:38 GMT
• Title: Further Analysis of Outlier Detection with Deep Generative Models
• Authors: Ziyu Wang, Bin Dai, David Wipf and Jun Zhu
• Abstract summary: Deep generative models can frequently assign a higher likelihood to outliers. We present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers.
• Score: 30.37180598197441
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The recent, counter-intuitive discovery that deep generative models (DGMs) can frequently assign a higher likelihood to outliers has implications for both outlier detection applications as well as our overall understanding of generative modeling. In this work, we present a possible explanation for this phenomenon, starting from the observation that a model's typical set and high-density region may not conincide. From this vantage point we propose a novel outlier test, the empirical success of which suggests that the failure of existing likelihood-based outlier tests does not necessarily imply that the corresponding generative model is uncalibrated. We also conduct additional experiments to help disentangle the impact of low-level texture versus high-level semantics in differentiating outliers. In aggregate, these results suggest that modifications to the standard evaluation practices and benchmarks commonly applied in the literature are needed.

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