Metric Learning

Metric Learning is a machine learning technique that focuses on learning a distance metric or similarity function from data, enabling more effective comparisons between data points. It aims to transform input data into a feature space where similar items are close together and dissimilar items are far apart, often using techniques like contrastive loss or triplet loss. This is widely used in tasks like image retrieval, face recognition, and recommendation systems to improve accuracy by optimizing distance measures.

Also known as: Distance Metric Learning, Similarity Learning, DML, Metric Learning Algorithms, Metric-based Learning

Why learn Metric Learning?

Developers should learn Metric Learning when working on applications that require similarity-based tasks, such as facial recognition, content-based image retrieval, or anomaly detection, as it enhances model performance by learning data-specific distance metrics. It is particularly useful in scenarios with high-dimensional data where traditional Euclidean distances may not capture meaningful relationships, and in supervised or semi-supervised settings to leverage labeled data for better discrimination.

Learning Resources

• docs: Metric Learning: A Survey
• tutorial: Deep Metric Learning with Python
• course: Metric Learning Course on Coursera
• video: Introduction to Metric Learning - YouTube
• tutorial: Hands-On Metric Learning with PyTorch