Self-supervised Learning

Self-supervised Learning is a type of machine learning where the model learns to predict a part of the input data from other parts of the same input data. It does not require explicit labels provided by humans. Instead, it uses the structure of the data itself to generate labels. For example, in NLP, a model might be trained to predict the next word in a sentence, using the previous words as input, thereby learning the syntax, semantics, and other language rules.

Synonyms:

Self-supervised

Papers published in this field over the years:

Hierarchy

Publications for Self-supervised Learning

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Researchers for Self-supervised Learning

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