Metric Learning

Metric Learning aims to learn a representation function that maps objects into an embedding space, where similar objects are closer to each other and dissimilar objects are farther apart. By optimizing specific loss functions, such as contrastive loss and triplet loss, Metric Learning can effectively capture the similarity between objects, thereby enhancing performance in tasks like time prediction.