A visual analytical approach for transfer learning in classification

Abstract Classification can be highly challenging when the dataset is extremely large, or when the training data in the underlying domain are difficult to obtain. One feasible solution to this challenge is transfer learning, which extracts the knowledge from source tasks and applies the knowledge to target tasks. Extant transfer learning schemes typically assume that similarities between the source task and the target task to some degree. This assumption does not hold in certain actual applications; analysts unfamiliar with the learning strategy can be frustrated by the complicated transfer relations and the non-intuitive transfer process. This paper presents a suite of visual communication and interaction techniques to support the transfer learning process. Furthermore, a pioneering visual-assisted transfer learning methodology is proposed in the context of classification. Our solution includes a visual communication interface that allows for comprehensive exploration of the entire knowledge transfer process and the relevance among tasks. With these techniques and the methodology, the analysts can intuitively choose relevant tasks and data, as well as iteratively incorporate their experience and expertise into the analysis process. We demonstrate the validity and efficiency of our visual design and the analysis approach with examples of text classification.