Fine-Grain Inference on Out-of-Distribution Data with Hierarchical Classification

Abstract

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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Cite

Text

Linderman et al. "Fine-Grain Inference on Out-of-Distribution Data with Hierarchical Classification." NeurIPS 2022 Workshops: MLSW, 2022.

Markdown

[Linderman et al. "Fine-Grain Inference on Out-of-Distribution Data with Hierarchical Classification." NeurIPS 2022 Workshops: MLSW, 2022.](https://mlanthology.org/neuripsw/2022/linderman2022neuripsw-finegrain/)

BibTeX

@inproceedings{linderman2022neuripsw-finegrain,
  title     = {{Fine-Grain Inference on Out-of-Distribution Data with Hierarchical Classification}},
  author    = {Linderman, Randolph and Zhang, Jingyang and Inkawhich, Nathan and Li, Hai and Chen, Yiran},
  booktitle = {NeurIPS 2022 Workshops: MLSW},
  year      = {2022},
  url       = {https://mlanthology.org/neuripsw/2022/linderman2022neuripsw-finegrain/}
}