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arXiv · 2306.14920

A Cosine Similarity-based Method for Out-of-Distribution Detection

Nguyen Ngoc-Hieu·Nguyen Hung-Quang·The-Anh Ta·Thanh Nguyen-Tang·Khoa D Doan·Hoang Thanh-Tung
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Abstract

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.

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Nguyen Ngoc-Hieu, Nguyen Hung-Quang, The-Anh Ta, Thanh Nguyen-Tang, Khoa D Doan, Hoang Thanh-Tung. 2023-06-23. A Cosine Similarity-based Method for Out-of-Distribution Detection. https://arxiv.org/abs/2306.14920

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