A survey on novel applications of Deep Metric Learning

Deep metric learning (DML) is a deep learning-based approach to similarity measurement. After the popular image-and vision-based deep learning applications showed that deep neural network-based feature extraction improves the performance of several classification tasks, different studies have started to investigate the application of deep networks for similarity evaluation. After high-performing results in facial recognition, DML has been used in other fields, including visual object tracking, multi-camera re-identification, anomaly detection and natural language processing. This paper presents a short, application-oriented survey on novel solutions that apply DML. The key contribution of this work is to address the gap between theoretical and application-oriented surveys using a structured methodology to identify emerging application domains with categorised use cases. The Scopus scientific database was used as the data source for this study, where a keyword-based filter was applied, and the result metadata was analysed.