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They are all related terms. From top to bottom: One-shot learning aims to achieve results with one or very few examples. Imagine an image classification task. You may show an apple and a knife to a human and no further examples are needed to continue classifying. That would be the ideal outcome, but for algorithms. In order to achieve one-shot learning (...


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The model has learnt the "features" for the type of inputs, eg. faces. For the problem to be called one-shot, it needs to also correctly classify/compare any new samples. For example, in face recognition application, any new person's images should also result a positive for their own image and negative for any other seen or unseen image. Since we are using ...


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