mlsanityData to 7 Oct 2026

Out-of-Distribution Detection Using Outlier Detection Methods

arXiv 2108.08218 ↗First posted 18 Aug 2021cs.LGcited by 1

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Abstract (arXiv)

Out-of-distribution detection (OOD) deals with anomalous input to neural networks. In the past, specialized methods have been proposed to reject predictions on anomalous input. Similarly, it was shown that feature extraction models in combination with outlier detection algorithms are well suited to detect anomalous input. We use outlier detection algorithms to detect anomalous input as reliable as specialized methods from the field of OOD. No neural network adaptation is required; detection is based on the model's softmax score. Our approach works unsupervised using an Isolation Forest and can be further improved by using a supervised learning method such as Gradient Boosting.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 18 of its 43 references that are in the map. First posted on arXiv 18 Aug 2021.

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18 of 43 references.

References · 18

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6 more
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  1. 2023Rule-based Out-Of-Distribution Detectioncited by 3