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.
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