Explaining zero-shot and one-shot learning in machine learning

Last Updated on September 11, 2022

Zeroshot learning and oneshot learning are both methods of machine learning where an algorithm can learn from a small amount of data. In zeroshot learning, the algorithm can learn from data that does not contain labels, while in oneshot learning, the algorithm can learn from data that only contains one label per data point.

Zeroshot learning is a relatively new method of machine learning that has shown promise in some applications. One example is image recognition, where the algorithm can learn to recognize objects from images that do not contain labels. Another example is text classification, where the algorithm can learn to classify documents without any labels.

Oneshot learning has been around for longer and is more commonly used. One example is facial recognition, where the algorithm can learn to recognize faces from a single image. Another example is handwritten digit recognition, where the algorithm can learn to recognize digits from a single image.

Both zeroshot learning and oneshot learning are powerful methods of machine learning that can be used to learn from small amounts of data.

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