A Survey on Deep Transfer Learning
It provides a comprehensive overview for researchers, but is incremental as it synthesizes existing work without introducing new methods.
This survey addresses the challenge of insufficient training data in domains like bioinformatics and robotics by reviewing deep transfer learning, which uses deep neural networks to relax the i.i.d. assumption and enable learning from limited datasets.
As a new classification platform, deep learning has recently received increasing attention from researchers and has been successfully applied to many domains. In some domains, like bioinformatics and robotics, it is very difficult to construct a large-scale well-annotated dataset due to the expense of data acquisition and costly annotation, which limits its development. Transfer learning relaxes the hypothesis that the training data must be independent and identically distributed (i.i.d.) with the test data, which motivates us to use transfer learning to solve the problem of insufficient training data. This survey focuses on reviewing the current researches of transfer learning by using deep neural network and its applications. We defined deep transfer learning, category and review the recent research works based on the techniques used in deep transfer learning.