A Visual Analytics Framework for Explaining and Diagnosing Transfer Learning Processes

Understand

Many statistical learning models hold an assumption that the training data and the future unlabeled data are drawn from the same distribution.

• However, this assumption is difficult to fulfill in real-world scenarios and creates barriers in reusing existing labels from similar application domains.
• Transfer Learning is intended to relax this assumption by modeling relationships between domains, and is often applied in deep learning applications to reduce the demand for labeled data and training time.
• Despite recent advances in exploring deep learning models with visual analytics tools, little work has explored the issue of explaining and diagnosing the knowledge transfer process between deep learning models.