# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 5 The Remaining Problems and Challenges
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Application Fields** & **Task Description** & **Typical Papers** \\ \hline Super Resolution (SR) & Converting image from low resolution & [17; 26; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 52; 53; 59; 54; 55; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 100; 103; 105; 107; 108; 109; 100; 101; 106; 107; 109; 101; 108; 109; 110; 111; 111; 112; 113; 114; 115; 116; 117; 118; 119; 120; 121; 122; 123; 124] \\ \hline Image style transfer & Transforming one type of image & \\  & into another style of image, such as & [15; 18; 25; 27; 35; 38; 39; 62; 87; 88; 89; 99] \\  & color transformation, texture transformation, etc. & \\ \hline Feature synthesis & Combining separate features & [19; 34; 38; 40; 41; 61; 71; 90; 91; 92] \\  & to make composite features & [19; 34; 38; 40; 41; 61; 71; 90; 91; 92] \\ \hline Image inpainting & Restoring the original appearance & \\  & of the image & [20; 55; 75; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104] \\ \hline Pose transfer & Generating images of the object & [21; 105; 106; 107; 108; 109; 110; 111; 112] \\  & observed from other angles & \\ \hline Image generation & Generating images according to text description & [16; 43; 44; 45; 58; 59; 60; 113; 114; 115; 116; 117; 118; 119; 120] \\  & & \\ \hline Other fields & Such as: single image dehazing, \\  & sequence labeling, inconsistency, etc. & [121; 122; 123; 124] \\ \hline \hline \end{tabular}
\end{table} 
Table 5: A summary of CGANs in various application fields. scientific research, some websites such as Kaggle have provided many types of data sets, but they are far from satisfying the research needs. * **The strong coupling with the task. ** Compared with the unconditional GAN, CGAN can be used to complete more tasks. However, the network used to solve one task is difficult to apply directly to another task. Despite that quite a few researchers summarized some design principles, such as the tricks summarized in Section 2.2, most of them only guided the overall framework of the network rather than a specific architecture inside the generative network and the discriminative network, which might have a great difference in different tasks. * **Neural Architecture Search (NAS)** It should be noted that in recent years, Neural Architecture Search (NAS) technology has made a lot of achievements in the field of image classification [125; 126], which is expected to help with the automatic design of network architecture. However, the complexity of CGAN means that there is still a long way to go, and even NAS technology can be employed. Furthermore, it should be reminded that, currently, CGAN is only a branch of GAN, and the performance improvement methods of CGAN [127; 128; 129; 130] are not significantly different from the traditional GAN. From the perspective of the application, CGAN's application scope is still very limited, except for computer vision. For example, in the field of natural language processing, published research results are comparatively much less than in the computer vision [131; 132]. The reason is the complexity of language itself and the discreteness of text data. In the future, overcoming these difficulties and designing a more efficient model will be the main tasks for natural language processing based on CGAN.