**5. Adversarial domain adaptation**

Instead of directly fine-tuning networks, adversarial domain adaptation is an appealing alternative to unsupervised learning. It mainly addresses the problem that there are abundant labeled data in the source domain but sparse/limited unlabeled samples in the target domain. The core idea of the adversarial domain adaptation is based on GANs. Specifically, a generalized architecture to implement this idea is proposed in [7]. In this section, we detail two main ideas: target data generating and domain classifier.

#### **5.1 Target data generating**

To overcome the limitation of sparse unlabeled data, target data generating is an approach to directly generate samples with labels for the target domain so that we can utilize them to train a classifier for the new task. One representative work is the CoGANs [25], in which there are two GANs involved: one for processing the labeled data in the source domain and another for processing the unlabeled data in the target domain. Part of the weights in the two generators is shared/tied in order to reduce the domain divergence. In addition to two discriminators for classifying the fake and real samples, there is also an extra classifier to classify the samples based on the information of labels in the source domain. By jointly training these two GANs, we can generate unlimited pairs of data, in which each pair consists of a synthetic source sample and a synthetic target sample and each pair shares the same label. Therefore, after finishing jointly training the two GANs, the pre-trained extra classifier is the function *F<sup>t</sup>* that we need for solving the new task. Similar work can also be found in [26], in which a transformation in the pixel space is introduced.

In summary, target data generating is a domain adaptation approach that focuses on generating target data, which can also be treated as an auxiliary task to reduce domain shift by a weight sharing mechanism between two GANs. The main disadvantage is that the training cost for generating synthesized samples with two GANs is expensive especially when the target datasets consist of large-size samples such as high-resolution images.
