Transfer Learning

This in-depth tutorial for students, researchers, and developers covers foundations, plus applications ranging from search to multimedia.

Qiang Yang (Author), Yu Zhang (Author), Wenyuan Dai (Author), Sinno Jialin Pan (Author)

9781107016903, Cambridge University Press

Hardback, published 13 February 2020

390 pages, 143 b/w illus.
23.5 x 15.6 x 2.1 cm, 0.73 kg

'This book offers a comprehensive overview of the field, arguing the case for adaptation as key to mimicking human intelligence … The book includes a substantial bibliography documenting copious citations to the literature. There appear to be few other textbooks in this field apart from this unique work. As such, it will be welcomed by libraries supporting strong computer science programs that may have need for a core text in artificial intelligence.' D. Z. Spicer, Choice

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

1. Introduction
2. Instance-based transfer learning
3. Feature-based transfer learning
4. Model-based transfer learning
5. Relation-based transfer learning
6. Heterogeneous transfer learning
7. Adversarial transfer learning
8. Transfer learning in reinforcement learning
9 Multi-task learning
10. Transfer learning theory
11. Transitive transfer learning
12. AutoTL: learning to transfer automatically
13. Few-shot learning
14. Lifelong machine learning
15. Privacy-preserving transfer learning
16. Transfer learning in computer vision
17. Transfer learning in natural language processing
18. Transfer learning in dialogue systems
19. Transfer learning in recommender systems
20. Transfer learning in bioinformatics
21. Transfer learning in activity recognition
22. Transfer learning in urban computing
23. Concluding remarks.

Subject Areas: Computer vision [UYQV], Machine learning [UYQM], Natural language & machine translation [UYQL], Mathematical theory of computation [UYA], Probability & statistics [PBT]