  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. In this paper, we present a visual analytics framework for the
multi-level exploration of the transfer learning processes when training deep
neural networks. Our framework establishes a multi-aspect design to explain how
the learned knowledge from the existing model is transferred into the new
learning task when training deep neural networks. Based on a comprehensive
requirement and task analysis, we employ descriptive visualization with
performance measures and detailed inspections of model behaviors from the
statistical, instance, feature, and model structure levels. We demonstrate our
framework through two case studies on image classification by fine-tuning
AlexNets to illustrate how analysts can utilize our framework.
