siamese network triplet loss

Siamese network triplet loss is a kind of loss function used in deep learning. It is mainly used in the training of siamese networks - a type of neural network that can compare and match two input data points. The triplet loss function takes three data points - an anchor, a positive example, and a negative example - and minimizes the distance between the anchor and positive examples, while maximizing the distance between the anchor and negative examples. This is achieved by calculating the Euclidean distance or cosine similarity between embeddings of each example. The goal of triplet loss is to learn a similarity metric that can correctly distinguish between similar and dissimilar examples.