Few-shot Learning

Few-shot Learning (FSL) refers to the ability of a machine learning model to understand and start performing a task with a very small amount of training data, typically just a few examples. This concept is inspired by the human ability to acquire new knowledge with a small number of examples. In NLP, this could mean learning to understand and generate text in a new language or understanding the sentiment of a sentence with just a few training examples.

Synonyms:

Few shot, Few-shot, FSL

Papers published in this field over the years:

Hierarchy

Publications for Few-shot Learning

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Researchers for Few-shot Learning

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