# Bidirectional-Feature-Learning-Based Adversarial Domain Adaptation with Generative Network
## 1 Introduction  
The field of domain adaptation is currently witnessing rapid progress in innovative research to address the problem of data mobility across different domains. In particular, recent advances in machine learning and deep learning techniques have attracted attention on how to overcome distributional differences between domains and improve the generalization performance of models [1, 2, 3, 4, 5, 6]. In particular, a growing body of research has focused on the relationship between domain adaptation and generative models. This research seeks to understand why gaps between adversarial domains occur and how to counteract them in order to make modelsmore robust. There has been a large body of research showing that approaches using generative models are useful for improving a model's generalization performance across domains [7]. Generative models mimic real-world data distributions to generate data in the target domain for adaptation, especially using generative adversarial neural networks (GANs) or autoencoder structures [7; 8; 9; 10; 11; 12; 13]. This paper utilizes an autoencoder structure in the generation model to reconstruct and utilize real image data. At this time, reconstructed image data can be treated as a kind of noisy image data caused by reconstruction error. They are used as input data. In addition, representation learning is a field that studies how to reconstruct data through feature extraction and selective feature transformation to address differences in features across domains. In view of these recent trends and research developments, this paper is expected to play an important role in presenting a new methodology that utilizes cross-domain bidirectional feature learning and generative networks to solve domain adaptation problems and overcome the limitations of existing studies. That is how I came across the model shown in panel (a) in Figure 1. This model is described in an interesting paper that allowed the authors to start studying domain adaptation. It is called DANN [1] for short, and it is a great paper to look at alongside its successor, the ADDA model [7], shown in panel (b) of Figure 1. Both models are adversarial models, but there is a difference. While DANN behaves adversarially when computing loss, the ADDA model initially learns to associate a discriminator with two domains to determine domain labels. If the domains had completely different characteristics, the loss would be extremely high at the beginning of training. However, the more the model is trained, the better the model will perform, i.e., it will be a better design. With this in mind, we wanted to build a model that is adversarial and has a bit more complexity. Table 1 summarizes what DANN and ADDA have in common and their differences from the model we propose in this paper. The reason why this table is important is because everything we will introduce in this paper is in this table. It starts with the question of whether there is a generative network or not. Then, there is adversarial learning, bidirectional feature learning, using a pretrained network (this is also related to training  
Figure 1: The different structures of DANN (**a**) and ADDA (**b**). time, which works a little differently in this work and will be discussed later), sharing weights, using a hyperparameter, etc. In the modern world, the rapid growth of data has led to the production of many different kinds of data in many different fields. However, these data often originate from different domains, each with unique characteristics and statistical distributions. This makes it difficult for machine learning models to generalize to new domains, which hinders the effective application of models in the real world. In response, domain adaptation research has gained increasing importance. In particular, recent research trends have explored innovative methods to address the problem of cross-domain portability. These approaches are mainly centered on generative models and representation learning [14; 15].