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This is a Experimental Study study.

October 18, 2025Open Access

Local Background Features Matter in Out-of-Distribution Detection

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The approach reduces overconfident predictions in out-of-distribution data, improving reliability.
Background features from in-distribution images are used as simulated OOD features during training.
Extensive experiments confirm new state-of-the-art performance in standard OOD detection benchmarks.
The method shows high compatibility with existing post-hoc OOD detection techniques.

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This method enhances OOD detection performance in neural networks, suggesting local background features reduce overconfidence on OOD data.

Ye et al. (2025) studied this question.

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This method enhances OOD detection performance in neural networks, suggesting local background features reduce overconfidence on OOD data.