What is Triplet Loss Function?
The triplet loss function is a comparison tool used in machine learning algorithms, which operates by contrasting a base input, a positive input, and a negative input. This tool aims to minimize the distance between the base and positive inputs while maximizing the distance between the base and negative inputs.
This model is integrated such that pairs with similar labels stay closer in comparison to pairs with differing labels, promoting distance order. Consequently, it often necessitates a soft margin approach, necessitating the use of an alpha display style slack variable in the hinge loss-style structure. Triplet loss is applied in applications like word embeddings, thought vectors, and metric learning for learning effective embeddings.
To expound on this, let's take a case of using triplet neural networks in facial recognition. Instead of modeling facial recognition as a classification problem, it could be modeled as a similarity-based learning task. For achieving results, the network needs in-depth training that helps to create a short distance for known images and a long distance for unknown images. Ultimately, sighting to generate a ranking instead of just similarities for picking out images that visually resemble a specific image, triplet loss is utilized.
In this context, a Euclidean distance function helps explain the loss. Here, triplets from the dataset apply- an anchor sample (xai), a positive example (xpi) with the same identifier as the anchor, and a negative example (xni) which represents a widely different individual from the anchor and positive instances. The triplet loss function trains the model to create embeddings that are more similar to the anchor for the positive example than for the negative example.
Contrastive loss vs. Triplet Loss
In modern computer vision, models converting images to deep and meaningful representations are highly utilized, with applications spanning zero-shot learning, visual search, face recognition, finest-grained retrieval, etc. Deep networks trained to respect pairwise relations are often the most efficient embedding models.
Deep embedding learning thrives on the concept of bringing related images closer within the embedding space and driving dissimilar ones apart. For instance, the contrastive loss ensures all positive images come together, while the negatives remain separated by a particular distance. But maintaining the same set distance for all images can prove limiting, causing distortions within the embedding space. This brought forward the triplet loss, which only requires the negative images to be more distanced from the positive images than any others.
This triplet loss leads among the best-performing losses on standard embedding tasks. Unlike pairwise losses, the triplet loss also influences the choice of positive and negative examples. Two primary differences allow the triplet loss to outperform the contrastive loss. First, the triplet loss doesn't utilize a threshold to distinguish between similar and different images. Instead, it can distort the space to cater to outliers and adapt to differing degrees of intra-class variance across different classes. Secondly, it merely needs positive instances to be closer than the negatives, while the contrastive loss aims to collect as many positive instances as possible, which isn't always necessary.
A contrastive loss, when used with a sampling process similar to triplet loss, greatly improves its performance, contradicting the common misunderstanding about the differences between the two losses. The effectiveness of triplet loss does not come from the function itself alone but stems from the sampling methods that accompany it.
Object tracking is an essential yet complex topic in many computer applications and the increasing attention from researchers to find superior features for better tracking accuracy is based on deep learning. Applications for the triplet loss in computer vision are diverse and include face recognition, image retrieval, and human re-identification.