Metric Learning

Metric learning aims to learn distance functions that effectively capture the relationships between data points, improving the performance of various machine learning tasks. Current research focuses on developing novel loss functions and algorithms, often incorporating techniques like contrastive learning, and adapting metric learning to diverse model architectures including Siamese networks, transformers, and hyperbolic embeddings, to address challenges in few-shot learning, anomaly detection, and cross-modal retrieval. These advancements have significant implications for various applications, such as recommendation systems, image retrieval, and out-of-distribution detection, by enabling more accurate and robust models. The field is also exploring the theoretical underpinnings of metric learning, seeking to understand its strengths and limitations in different contexts.