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
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.