Triplet Loss
A core objective function in metric learning that takes an anchor, a positive sample and a negative sample at each step. Optimization makes the embedding distance between the anchor and the positive sample smaller than that between the anchor and the negative sample. It is commonly used in face recognition and image retrieval.
Timeline (last 90 days)
Triplet Loss takes one triplet (anchor, positive sample, negative sample) at a time, optimizing so that the embedding distance between the anchor and the positive sample is smaller than the distance between the anchor and the negative sample, and usually adds a margin to prevent degenerate solutions.
Commonly used loss functions for contrastive learning include InfoNCE Loss and Triplet Loss.
All Facts (2)
Commonly used loss functions for contrastive learning include InfoNCE Loss and Triplet Loss.
60%UnverifiedTriplet Loss takes one triplet (anchor, positive sample, negative sample) at a time, optimizing so that the embedding distance between the anchor and the positive sample is smaller than the distance between the anchor and the negative sample, and usually adds a margin to prevent degenerate solutions.
50%