Bayesian Transfer Learning: An Overview of Probabilistic Graphical Models for Transfer Learning

Understand

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.