*2.2. Domain Adaptive Technology (DA)*

As shown in Figure 2, domain adaptation is used to map data features from different domains to the same feature space, so that other domain data can be used to enhance the target domain training. There are two fundamental concepts in domain adaptation: the source domain and the target domain. The source domain, *DS* = {*XS*, *P*(*XS*)}, is rich in supervised learning information. The target domain, *DT* = {*XT*, *P*(*XT*)}, represents the

domain in which the test set is located, usually without labels or with only a few labels. Source and target domains are often the same type of task but are distributed differently.

**Figure 2.** Domain adaptation.

Common domain adaptation methods include:


Domain loss is calculated using the maximum mean difference (MMD). To be specific, the transferable features are first mapped into reproduced kernel Hilbert space (RKHS), in which the mean distance between them is viewed as the metric to their distribution discrepancy:

$$MMD(\mathbf{X}\_{\mathcal{S}}, \mathbf{X}\_{\mathcal{T}}) = \left\| \frac{1}{|\mathbf{X}\_{\mathcal{S}}|} \sum \phi(\mathbf{x}\_{\mathcal{S}}) - \frac{1}{|\mathbf{X}\_{T}|} \sum \phi(\mathbf{x}\_{\mathcal{t}}) \right\| \tag{8}$$

where *φ* is a mapping function, *XS* is the source data, and *XT* is the target data.
