**7. Applications**

As shown in **Figure 1**, the scope of transfer learning is far beyond traditional machine learning. Theoretically, the problems addressed by deep learning can also be solved by transfer learning. In this section, we narrow the discussion to the typical real-world applications based on deep domain adaptation. In Section 7.1, we summarize the most methods discussed above for computer vision. In Section 7.2, we discuss the applications beyond the context of image processing, including natural language processing, speech recognition and other real-world applications based on processing time-serial data.

#### **7.1 Applications in computer vision**

#### *7.1.1 Image classification and recognition*

Classification is a fundamental and most basic problem in machine learning, most of the above methods are introduced to address this problem. Therefore, we pay our attention to the advances that deep domain adaptation can bring for image classification, rather than repeatedly introducing them. Probably the most wellknown example is fine-tuning a giant network that is pre-trained with the ImageNet dataset for real-world applications such as pet recognition. Despite the fact that manually collecting data is time-consuming and expensive, the data collected from the real-world is usually imbalanced (e.g., there are only 100 images of class A but 10,000 images of class B). If we train a classifier from scratch, the performance can be poor because it cannot learn enough knowledge from the limited samples (e.g., class A). However, if we utilize a pre-trained model based on the well-collected ImageNet and fine-tune it, the problem caused by an imbalance dataset will be

## *Transfer Learning and Deep Domain Adaptation DOI: http://dx.doi.org/10.5772/intechopen.94072*

reduced because the model has already obtained rich knowledge from the source domain.

Another typical real-world application that we can gain benefits from domain adaptation is face recognition. A general approach to solve this problem is to train a model based on a dataset of labeled face images. In contrast, the large-scale unlabeled video datasets are always available. However, the divergence of data in the video is usually limited and there remains a clear gap between these two different domains. In order to utilize the rich information from video and overcome these challenges, the authors in [31] propose a framework for face recognition in unlabeled video based on the adversarial domain adaptation approach.
