Designing Out-of-distribution Data Detection using Anomaly Detectors: Single Model vs. Ensemble

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.