Paper abstract

Actively Transfer Domain Knowledge

Xiaoxiao Shi - Sun Yat-sen University, China
Wei Fan - IBM, TJ Watson Research Center, USA
Jiangtao Ren - Sun Yat-sen University, China

Session: Transfer Learning
Springer Link:

Active learning and transfer learning are separate efforts to deal with label deficiency for inductive learning. Active learning asks domain experts to label a small set of examples, but there is a cost incurred for each answer. While transfer learning could borrow labeled examples from a different domain without incurring any labeling cost, there is no guarantee that the transferred examples will help improve the learning accuracy. We propose a framework to actively transfer the knowledge across domains. To do so, labeled examples from the other domain are examined based on their likelihood to correctly label the examples of the current domain. When this likelihood is low, these out-of-domain examples will not be used to label the in-domain example, but domain experts are consulted to provide class label. We theoretically and empirically prove that the proposed method can effectively mitigate risk of domain difference and query experts only when necessary.