Human activity recognition with deep metric learners.
Kyle Martin, Anjana Wijekoon, Nirmalie Wiratunga · Open Access Institutional Repository at Robert Gordon University (Robert Gordon University) · 2020
Establishing a strong foundation for similarity-based return is a top priority in Case-Based Reasoning (CBR) systems. Deep Metric Learners (DMLs) are a group of neural network architectures which learn to optimise case representations for similarity-based return by training upon multiple cases simultaneously to incorporate relationship knowledge. This is particularly important in the Human Activity Recognition (HAR) domain, where understanding similarity between cases supports aspects such as personalisation and open-ended HAR. In this paper, we perform a short review of three DMLs and compare their performance across three HAR datasets. Our findings support research which indicates DMLs are valuable to improve similarity-based return and indicate that considering more cases simultaneously offers better performance.