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- W3096548319 abstract "Image classification neural networks tend to give high probabilities to images they in fact do not recognize. This paper compares three approaches to detect such out-of-distribution data: One-Class Support Vector Machine, Isolation Forest and Local Outlier Factor. The experiments show that Isolation Forest outperforms the other two approaches. However, when combining the three algorithms using a majority voter, the results show that this ensemble is better at detecting out-of-distribution data than using the Isolation Forest algorithm solely." @default.
- W3096548319 created "2020-11-09" @default.
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- W3096548319 date "2020-09-16" @default.
- W3096548319 modified "2023-10-02" @default.
- W3096548319 title "Designing Out-of-distribution Data Detection using Anomaly Detectors: Single Model vs. Ensemble" @default.
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- W3096548319 doi "https://doi.org/10.1109/et50336.2020.9238227" @default.
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