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Poster
in
Workshop: The Second Workshop on Spurious Correlations, Invariance and Stability

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

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


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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