Triplet Loss is a sophisticated loss function meticulously crafted to drive the embeddings of similar data points to converge within the feature space, while compelling the embeddings of dissimilar data points to diverge. The concept of "triplet" denotes a set of three data points: an anchor point, a positive point, and a negative point. The anchor point serves as the focal data point around which an embedding is to be learned. The positive point represents a data point sharing similarities with the anchor, such as an image of the same person. Conversely, the negative point is a data point that is distinctly different from the anchor, like an image of a completely different person.
