Deep Metric Learning - Techniques and Applications: Investigating deep metric learning techniques for learning similarity metrics directly from data for tasks such as image retrieval

Abstract

Deep metric learning (DML) has gained significant attention in recent years for its ability to learn similarity metrics directly from data. By leveraging deep neural networks, DML techniques can effectively capture complex relationships between data points, making them well-suited for tasks such as image retrieval. This paper provides a comprehensive overview of DML techniques, including siamese networks, triplet networks, and contrastive loss, among others. We also discuss the applications of DML in various domains, such as image retrieval, face verification, and person re-identification. Additionally, we highlight the challenges and future directions in DML research, including scalability and interpretability. Overall, this paper aims to provide a comprehensive understanding of DML techniques and their applications, serving as a valuable resource for researchers and practitioners in the field of computer vision and machine learning.