On Deep Domain Adaptation: Some Theoretical Understandings

Understand

Compared with shallow domain adaptation, recent progress in deep domain adaptation has shown that it can achieve higher predictive performance and stronger capacity to tackle structural data (e.g., image and sequential data).

• The underlying idea of deep domain adaptation is to bridge the gap between source and target domains in a joint space so that a supervised classifier trained on labeled source data can be nicely transferred to the target domain.
• This idea is certainly intuitive and powerful, however, limited theoretical understandings have been developed to support its underpinning principle.
• In this paper, we have provided a rigorous framework to explain why it is possible to close the gap of the target and source domains in the joint space.