Transfer Learning

Transfer learning adapts knowledge from a pretrained model to a new target task, often reducing the amount of task-specific data and computation required.

Japanese terms

1. Transfer learning — 転移学習てんいがくしゅう: Reusing knowledge learned for one task or domain to improve learning on another.
2. Pretrained model — 事前学習済じぜんがくしゅうずみモデル: A model whose parameters were learned before adaptation to the current task.
3. Target task — 対象たいしょうタスク: The task to which transferred knowledge or a pretrained model is being applied.

Related topics

Foundation models are frequently adapted through transfer learning and fine-tuning.