Transfer learning

Transfer learning is an attempt to reuse concepts which have  been previosuly encoded in a machine learning model for a new task. For example, in machine learning training, often two phases are involved: one pretraining, where the model picks up general information, and finetuning, where the model learns domain specific knowledge. General world knowledge is transferred from the first stage to the second. (see pretraining, finetuning).

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