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One Shot and Metric Learning - Quadruplet Loss (Machine Learning Dojo)

Machine Learning Street Talk (MLST)

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Revolutionizing Image Recognition with Contrastive Learning

This chapter explores the limitations of traditional supervised classification in image recognition, particularly for recognizing vast numbers of individual classes. It emphasizes the shift towards similarity comparisons using Siamese networks and contrastive loss, demonstrating how neural networks like BERT can facilitate efficient identity verification through one-shot learning.

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