Few-shot learning is a term from the field of artificial intelligence and digital transformation. It describes a method in which an artificial intelligence learns to solve new tasks with only a very few examples – sometimes as few as three to five. In contrast to traditional learning methods, which require thousands of training data, few-shot learning saves time and resources.
Imagine you want to teach software to recognise different types of exotic flowers. With traditional methods, you would have to show the AI thousands of photos of each flower. With Few-Shot Learning, only a few images are enough for the software to recognise the flower independently in the future.
This offers significant advantages: less data means less effort in collecting and preparing training material. Few-shot learning is particularly useful wherever data is scarce – for example, for rare diseases in medicine or for recognising new products in digital commerce.
Ultimately, few-shot learning helps companies integrate AI applications into their processes more quickly and efficiently, and to react more flexibly to new developments.
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