Transfer learning (TL) is a research problem in data science that is mainly concerned with persisting knowledge acquired during solving a specific task and using this acquired knowledge to solve another different but similar task. In this chapter, we will demonstrate one of the modern practices and common themes used in the field of data science with TL. The idea here is how to get the help from domains with very large datasets to domains that have less dataset size. Finally, we will revisit our object detection example of CIFAR-10 and try to reduce both the training time and performance error via TL.

The following topics will be covered in this chapter:

• Transfer learning
• CIFAR-10 object detection revisited