Triplet Loss

What is Triplet Loss?

The triplet loss function compares a baseline input to positive input and a negative input in machine learning algorithms. The distance between the baseline input and the positive input is reduced to a minimum, while the distance between the baseline input and the negative input is increased.

Triplet loss models are embedded in the way that a pair of samples with the same labels are closer than those with different labels by enforcing the order of distances.

As a result, it requires soft margin treatment with a slack variable display style alpha in its hinge loss-style formulation in its most frequent implementation. Word embeddings, thinking vectors, and metric learning are examples of how it is used to learn similarity for learning embeddings.

Take, for example, the job of teaching triplet neural networks to detect faces. Instead of describing the problem as a classification problem, it might be posed as a learning method based on similarity tasks. The network has undergone comprehensive training in order to create a distance that is small when the image belongs to a known figure and big when the image belongs to an unidentified individual. If we want to produce the data, we’ll need to learn a rating rather than just a similarity in order to select the most similar images to a particular image. In this scenario, a triplet loss is employed.

A Euclidean distance function can be used to explain the loss. The function works with triplets, as seen in the following three samples from the dataset:

xai – an example of an anchor A photograph of a person’s face is one example.

xpi – a positive example with the same identification as the anchor. This is the second photograph of exactly the same individual as the one in the anchoring example.

xni – a negative representation of a dissimilar entity. This would be a picture of a widely differing individual than the one represented by the anchor and positive examples.

  • The triplet loss function is used to train the model to produce embeddings that are closer to the anchor for the positive case than for the negative case

Triplet loss vs contrastive loss

Modern computer vision relies on models that turn images into rich, semantic representations, with applications ranging from zero-shot learning and visual search to face recognition and fine-grained retrieval. The most successful embedding models are deep networks that are trained to respect pairwise relationships.

Deep embedding learning is based on the simple principle of bringing comparable images closer together in embedding space while pushing dissimilar ones apart. The contrastive loss, for example, compels all positive images to be near together, whereas all negatives to be separated by a specified distance.

Using the same set distance for all images, on the other hand, can be restrictive, preventing any distortions in the embedding space. This prompted the triplet loss, which only necessitates negative images being further away from any positive images.

On conventional embedding tasks, this triplet loss is now among the best-performing losses. The triplet loss, unlike pairwise losses, does not merely change the function; it also alters how positive and negative examples are chosen.

Two major differences explain why triplet loss surpasses contrastive loss in general: The triplet loss does not use a threshold to distinguish between similar and dissimilar images. Instead, it can distort the space to accommodate outliers and adjust to various degrees of intra-class variance for distinct classes.

Second, the triplet loss merely requires positive instances to be closer than negative examples, whereas the contrastive loss focuses on gathering as many positive examples as feasible. The latter isn’t required. For most applications, such as image retrieval, grouping, and verification, retaining the right relative connection is sufficient.

While contrastive loss produces far lower results with random sampling than triplet loss, its performance improves greatly when utilizing a sampling process comparable to triplet loss. This information disproves a widespread misperception about the differences between triplet loss vs contrastive loss.

  • The strength of triplet loss implementation derives not just from the function itself, but also from the sampling procedures that go with it.


Object tracking is a vital and difficult subject in many computer vision applications. As a result of this problem, an increasing number of academics are focusing on using deep learning to obtain stronger features for improved tracking accuracy. Face recognition, image retrieval, and human re-identification are just a few of the applications of triplet loss in computer vision.