triplet loss
Triplet loss is a loss function used in deep learning for face recognition and similarity measurement tasks. It aims to optimize the feature embeddings of three samples: an anchor, a positive, and a negative sample. The loss function encourages the distance between the anchor and positive sample to be smaller than the distance between the anchor and negative sample by a predefined margin. This helps in learning discriminative embeddings that can accurately compare and classify similar and dissimilar samples.
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