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Deep learning models keep getting more and more sophisticated and are very good at analyzing patterns and discriminative features in datasets. However, they usually need a large amount of data to converge, and this amount of data is not always available. We can think of examples like signature-verification where a system only has one or two examples of a customer’s signature or face recognition where only one portrait of the person might be available.

One-shot learning is the classification task where a model has to predict the label of inputs without having trained on the class involved at all. For…

Axel Xu

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