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Further Analysis of Outlier Detection with Deep Generative Models
Ziyu Wang · Bin Dai · David P Wipf · Jun Zhu

Thu Dec 10 09:00 AM -- 11:00 AM (PST) @ Poster Session 5 #1542

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.

Author Information

Ziyu Wang (Tsinghua University)
Bin Dai (Samsung Research China - Beijing)
David P Wipf (AWS)
Jun Zhu (Tsinghua University)

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