Triplet Loss is a loss function used in machine learning, particularly in the context of training neural networks for tasks involving learning similarities and differences between inputs, such as face recognition and similarity learning.
How Triplet Loss Works
Triplet loss operates on three data points at a time (a triplet) - an anchor, a positive example (similar to the anchor), and a negative example (dissimilar to the anchor). The goal of the loss function is to ensure that the anchor is closer to the positive example than to the negative example in the learned feature space. This is typically used in tasks where the relationships between data points are more important than their individual categorizations.
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