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Fine-grain Inference on Out-of-Distribution Data with Hierarchical Classification
Randolph Linderman · Jingyang Zhang · Nathan Inkawhich · Hai Li · Yiran Chen

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in Workshop on Machine Learning Safety »

Machine learning methods must be trusted to make appropriate decisions in real-world environments, even when faced with out-of-distribution (OOD) samples. Many current approaches simply aim to detect OOD examples and alert the user when an unrecognized input is given. However, when the OOD sample significantly overlaps with the training data, a binary anomaly detection is not interpretable or explainable, and provides little information to the user. We propose a new model for OOD detection that makes predictions at varying levels of granularity—as the inputs become more ambiguous, the model predictions become coarser and more conservative.

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Author Information

Randolph Linderman (Duke University)
Jingyang Zhang (Duke University)
Nathan Inkawhich (Air Force Research Laboratory)
Hai Li (Duke University)
Yiran Chen (Duke University)

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