Deep Metric Learning for Image Similarity: Exploring deep metric learning techniques for measuring similarity between images in embedding spaces learned by neural networks

Abstract

Deep metric learning has emerged as a powerful technique for learning similarity metrics directly from data, particularly in the context of image similarity. This paper provides an overview of deep metric learning methods for measuring image similarity in learned embedding spaces. We discuss various deep learning architectures and loss functions used in deep metric learning and review applications in image retrieval, clustering, and classification. We also examine challenges and future directions in the field, including interpretability and scalability. The paper concludes with a discussion on the potential impact of deep metric learning on computer vision and related fields.