AI glossary · Machine learning
Transfer learning
Transfer learning reuses what an AI model learned on one task as the starting point for a related task, so the new task needs much less data and training.
In more detail
A model trained to recognise everyday objects already understands shapes and textures. Adapting it to spot plant diseases takes far fewer examples than starting from scratch.
Fine-tuning a foundation model is the most common form of transfer learning today.
Related terms
Fine-tuningFine-tuning is extra training that adapts an existing AI model to a specific task, style or field, using a smaller, focused set of examples.Foundation modelA foundation model is a large AI model trained on broad data that can be adapted to many different tasks, from chat to coding to image analysis.Pre-trainingPre-training is the first, biggest stage of training an AI model, where it learns general patterns from a huge dataset before being adapted for specific uses.
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