Metric Learning
Metric learning involves training a model to learn a distance function or similarity measure between data points, often by transforming data into an embedding space where similar items are closer and dissimilar items are further apart. This is typically achieved through techniques like triplet loss or contrastive loss, which penalize small distances for dissimilar pairs and large distances for similar pairs.
It's like teaching a computer to judge how 'alike' two things are, so that apples are consistently closer to other apples than to oranges in a mental map.
It's crucial for tasks like recommendation systems, face recognition, and clustering where the definition of 'similarity' is key but not obvious.
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