The Beginner’s Guide to Contrastive Learning

Here's everything you need to know about Contrastive Learning and its most prominent applications. Ready to dive in?
Read time
19
min read  ·  
March 2, 2023
Contrastive Learning framework example

Contrastive Learning is a technique that enhances the performance of vision tasks by using the principle of contrasting samples against each other to learn attributes that are common between data classes and attributes that set apart a data class from another.

This mode of learning, which mimics the way humans learn about the world around them, has shown promising results in the Deep Learning literature, thus gaining importance in the field of Computer Vision research.

You are about to read the most comprehensive article on Contrastive Learning.

Here’s what we’ll cover:

  1. What is Contrastive Learning?
  2. The importance of Contrastive Learning
  3. How does Contrastive Learning work?
  4. Ten Contrastive Learning frameworks
  5. Applications of Contrastive Learning

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Rohit Kundu is a Ph.D. student in the Electrical and Computer Engineering department of the University of California, Riverside. He is a researcher in the Vision-Language domain of AI and published several papers in top-tier conferences and notable peer-reviewed journals.

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