Contrastive Learning
Contrastive learning is a self-supervised approach where a model learns representations by comparing similar and dissimilar pairs of data. It pulls 'positive' pairs (different views of the same instance) closer in the embedding space while pushing 'negative' pairs (different instances) further apart. This helps the model discern meaningful features without explicit labels.
Imagine teaching a child to recognize a dog by showing them many different pictures of dogs and many different pictures of other animals, emphasizing what makes dogs similar to each other and different from everything else.
It allows models to learn powerful representations from vast amounts of unlabeled data, crucial for tasks where labeled data is scarce.
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