Transfer Learning Theory for Deep Leaning in Machine Learning

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. This post focuses on discussing the theory of transfer learning, under Deep Learning (DL) in Machine Learning (ML) context from the educational aspect.

The first theory of transfer learning is the theory of identical elements. This theory of identical elements has been established by Edward Thorndike. Based on his knowledge, nearly all transfer happen from one situation to another situation, where there is almost similar or identical elements. This theory can be described as, the more the similarity of the task or situation, the more transfer can be done. For example, people who already know how to ride a bicycle, can learn to ride a motorcycle faster because both vehicles share the similar element. As you can imagine how useful for implementing this for deep learning, under machine learning context.

The second theory is the theory of generalization of experience. This theory has been unfold by Charles Hubbard Judd. This theory of generalization conclude that when transfer what we learn in task A can be applied in task B because when studying task B it implies that the general principle in both task A and B are part or completely the same. In the theory of generalization, experiences, habits and knowledge can be generalized and used with other situation. People need to recognize and understand what is common to a number of situations in generalization. The competence of individuals to generalize knowledge depend on their intelligence. But in the domain of Artificial Intelligence (AI) and Machine Learning (ML), the works for the Transfer Learning in Deep Learning context is use for explore more complex human-like for artificial intelligence (AI) x machine learning (ML) what is possible.

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