A Cosine Similarity-based Method for Out-of-Distribution Detection
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Abstract (arXiv)
The ability to detect OOD data is a crucial aspect of practical machine learning applications. In this work, we show that cosine similarity between the test feature and the typical ID feature is a good indicator of OOD data. We propose Class Typical Matching (CTM), a post hoc OOD detection algorithm that uses a cosine similarity scoring function. Extensive experiments on multiple benchmarks show that CTM outperforms existing post hoc OOD detection methods.
Key facts. Cited by 2 papers in the map (data through 7 Oct 2026). Builds on 22 of its 29 references that are in the map. First posted on arXiv 23 Jun 2023.
Builds on
22 of 29 references.
References · 22
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10 more
- 2014Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Imagescited by 774
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Citing papers per year: 2025: 1; 2026: 1.
2 papers.