# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 1 Introduction
Generative models focus on overcoming distributional differences while generating data in the target domain. Studies utilizing generative adversarial neural networks (GANs) or autoencoder structures have received much attention. Representation learning also focuses on performing adaptation in a way that reduces differences in characteristics between domains through feature extraction and transformation. However, most of the existing research has focused on unidirectional feature transformations. In the learning processes of previous studies that learn by transforming features themselves, limitations and problems arise when trying to adapt the target domain to a model trained on the source domain. In contrast, this paper explores the domain adaptation problem from a new angle by combining a bidirectional feature learning process and generative network and proposes an innovative methodology to perform adaptation while mutually preserving features between two domains. It is expected to more accurately consider the distribution of real data, overcome the limitations of existing methods in effectively solving the domain adaptation problem, deepen our understanding of the relationship between generative models and domain adaptation and features, and contribute to the development of more robust and stable models. The starting point of this paper is the need for domain adaptation and the current state of research. The problem of the cross-domain mobility of data collected from various fields limits the performance of machine learning models. As a solution, existing research has mainly focused on unidirectional feature transformations. However, starting from the idea that the relationship between domains could be bi-directional, we tried to introduce a bi-directional feature learning process. We found that the bi-directional feature learning method, which is the training method of our proposed model, has been used in several studies. However, the most important concepts in the field of domain adaptation are learning in the direction of minimizing empirical risk [16], feature conversion using image-to-image translation [10; 17], transfer learning [18; 19; 20; 21], reducing the gap between domains with generalization and robustness of the model and generative networks [12], adversarial learning with discriminators, adversarial learning without discriminators [22], pseudo-labeling for domain adaptation in the absence of discriminative classifiers [23], and so on, all of which seem to be similar to domain adaptation. However, they have slightly different contributions. Thus, there is still a lot of potential for further development. To advance this idea, it is necessary to strengthen the role of generative networks. Generative models need \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **DANN** & **ADAA** & **BiFLP-AdvDA** \\ \hline Generative network & x & x & o \\ \hline Adversarial learning & o & o & o \\ \hline Bidirectional feature learning & x & x & o \\ \hline Pretrained network & x & o (CNN) & o (autoencoder) \\ \hline Weight sharing & x & x & o \\ \hline Hyperparameter & \(\lambda\) & \(\lambda\) & \(\lambda\), m (margin) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of recent methods (o denotes method is used in model; x denotes it is not used). to be improved to simulate more realistic data distributions. Although such research studies have been ongoing, they have not been able to move beyond a one-way learning process. To this end, we propose a model combining generative networks and bidirectional feature learning processes to perform both functions together [24]. Furthermore, bidirectional feature learning can serve as a potential way to enforce interdependence between two domains. In this way, we devised a novel network architecture that could achieve good adaptation while preserving features between the two domains. From the perspective of generative models, new ideas can be generated on how to reconfigure different data to adapt to new domains.