Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning
It addresses the need for better tools in AI to handle uncertainty and interpretability in transfer learning, but is incremental as it primarily surveys and proposes directions rather than introducing new methods.
This paper tackles the underutilization of probabilistic graphical models (PGMs) in transfer learning by reviewing existing PGM-based methods, discussing real-world applications, and suggesting future research directions to enhance their development.
Transfer learning where the behavior of extracting transferable knowledge from the source domain(s) and reusing this knowledge to target domain has become a research area of great interest in the field of artificial intelligence. Probabilistic graphical models (PGMs) have been recognized as a powerful tool for modeling complex systems with many advantages, e.g., the ability to handle uncertainty and possessing good interpretability. Considering the success of these two aforementioned research areas, it seems natural to apply PGMs to transfer learning. However, although there are already some excellent PGMs specific to transfer learning in the literature, the potential of PGMs for this problem is still grossly underestimated. This paper aims to boost the development of PGMs for transfer learning by 1) examining the pilot studies on PGMs specific to transfer learning, i.e., analyzing and summarizing the existing mechanisms particularly designed for knowledge transfer; 2) discussing examples of real-world transfer problems where existing PGMs have been successfully applied; and 3) exploring several potential research directions on transfer learning using PGM.