What is Triplet Loss
In our previous exploration of Contrastive Learning (opens new window) , we uncovered how models can learn to differentiate between similar and dissimilar data by bringing like items closer and pushing unlike ones apart in an embedding space. We discussed methods like SimCLR (opens new window) , MoCo (opens new window) , and CLIP (opens new window) , which have significantly advanced self-supervised learning (opens new window) .
Continuing this journey into metric learning, let’s talk about Triplet Loss. It builds upon the principles of contrastive learning and plays a crucial role in tasks that require fine-grained distinctions, such as face recognition, image retrieval, and signature verification.
Before we dive into Triplet Loss, it's important to understand Metric Learning. Metric learning is a type of machine learning that focuses on learning a distance function (or metric) that measures similarity between data points. The core idea is simple:
We use a machine learning model to generate embeddings, then train the model to minimize the distance between similar data points while maximizing the distance between data points that belong to different categories or labels.
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