Triplet loss

Function for machine learning algorithms

Triplet loss is a loss function for machine learning algorithms where a reference input is compared to a matching input and a non-matching input. The distance from the anchor to the positive is minimized, and the distance from the anchor to the negative input is maximized. An early formulation equivalent to triplet loss was introduced for metric learning from relative comparisons by M. Schultze and T. Joachims in 2003.

This keyword could refer to multiple things. Here are some suggestions: