Contrastive Learning: A Tutorial | Built In
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Contrastive Learning: A Tutorial | Built In
Contrastive learning is a machine learning technique that trains models to differentiate between similar and dissimilar data pairs. It is used for self-supervised learning, computer vision, natural language processing and other use cases.
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Artificial Intelligence | AI on Instagram: "Contrastive learning is a type of self-supervised learning where the goal is to learn representations by comparing pairs of data. Instead of predicting missing parts of data like other self-supervised algorithms, it teaches a model to bring similar examples (called positive pairs) closer in the embedding space while spacing different ones (called negative pairs) farther apart. For instance, two different versions of the same image (rotated and cropped)
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Contrastive learning is common for folks working in NLP and images. This was new to me, so wanted to share the intuition a bit more widely. This is an introduction, there are so many contrastive loss functions for different scenarios.
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