**1. Introduction**

Inspired by the biological neurons, deep neural networks are well known for their ability to learn data representation from a huge amount of labeled data such as the famous convolutional neural networks (CNNs). Specifically, given a specific task such as image classification, we usually need to train a deep neural network from scratch with enough training data so that our model can achieve acceptable performance. However, sufficient training data for a new task is not always available as manually collecting and annotating data are labor-intensive and expensive. Especially in some specific domains such as healthcare, a privacy concern is also raised. Meanwhile, training a deep network with a large dataset is usually timeconsuming and involves huge computational resources. Intuitively, it is not realistic and practical to learn from zero, because the real way we humans learn is that we usually try to solve a new task based on the knowledge obtained from past experiences. For example, once we have learned a programming language (e.g., Java), we can easily learn a new one (e.g., Python) as the basic programming fundamentals are the same.

Transfer Learning is an inspiring method that can help apply the knowledge gained from a source task to a new/target task. Specifically, the goal of transfer learning is to obtain some transferable representations between the source domain and target domain and utilize the stored knowledge to improve the performance on the target task. Note that transfer learning is an extensive research topic that involves many learning methods. In particular, deep domain adaptation gets the most attention in recent years among these methods. Therefore, after briefly introducing the transfer learning in this research, we pay our attention to analyzing and discussing the recent advances in deep domain adaptation.

The rest of this chapter is structured as follows. In Section 2, we give an overview and specific definitions of transfer learning. In Section 3, we summarize the main approaches for deep domain adaptation. In Section 4, 5 and 6, we discuss the details for conducting deep domain adaptation. The recent applications based on deep domain adaptation methods are also introduced in Section 7. Finally, we conclude this research and discuss future trends in Section 8.
