Transfer learning and transfer knowledge are related but not exactly the same concepts in AI. According to Wikipedia, transfer learning is a research problem in machine learning that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem. For example, knowledge gained while learning to recognize cars could be applied when trying to recognize trucks.
Transfer knowledge, on the other hand, is a more general term that can refer to any process of transferring information or skills from one domain or task to another. Transfer learning is one specific way of doing transfer knowledge using machine learning models. Other ways of doing transfer knowledge include knowledge distillation, which is a technique of compressing a large model into a smaller one by mimicking its behavior, or semi-supervised learning, which is a method of using unlabeled data along with labeled data to improve the performance of a model.
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