A system can learn general representations or principles in one setting and adapt them with fewer examples in another. Pretraining followed by fine-tuning is a common form, but transfer can also occur through reusable skills, features or policies.
Transfer succeeds when the earlier knowledge is relevant to the new problem. A mismatch can cause negative transfer, where inherited assumptions slow learning or reduce performance, so the adapted system needs direct evaluation.
ELI5
Transfer learning lets an AI use lessons from earlier work when it faces a new task. It avoids starting from nothing each time.
For example, a model that learned general visual shapes can adapt to identify a new kind of equipment from a smaller dataset. The old knowledge helps only if it matches the new setting, so engineers test for inherited mistakes.
