# Conditional Generative Adversarial Networks for Domain Transfer: A Survey
## 4 The Applications of CGAN
### Super Resolution (SR)  
High-resolution images are widely used in the production industry and daily life. However, due to the large size of their corresponding files, it presents great challenges for stable storage and efficient transmission. Therefore, a feasible method is to use the low-resolution images in the process of image storage and transmission, and when the image needs to be employed, the high-resolution image will be restored according to the low-resolution image. Therefore, Single Image Super Resolution (SISR) has become a research hot spot in the field of computer vision [28].  
SRGAN was the first work to solve the SR problem via a CGAN model [30]. In SRGAN, the generative network consists of several jump connections to synthesize the low-level and high-level semantics, and two sub-pixel convolutional layers are used to improve the image resolution. In terms of the loss function, SRGAN uses perceptual loss in addition to adversarial loss, where the perceptual loss is calculated by the feature map of the VGG network (Oxfords Visual Geometry Group's pre-training network) [29].  
In recent years, Curriculum Learning (CL) [76] has received more and more attention, the main idea of which is to feed the training data to the model step-by-step rather than all at once. It has achieved great success in many tasks such as natural language processing [77; 78], image recognition [79] and even image generation [80]. Inspired by curriculum learning, the ProGanSR has been proposed, and it indicates that the learning direction should be gradually transformed from a small level to a large level. ProGanSR uses an asymmetric pyramid structure to achieve efficiency. Each pyramid consists of a dense compression unit and a sub-pixel convolutional layer to double up the input resolution. [PERSON] et al. proposed a local significance diagram defining the importance of each pixel, which could be used in the adversarial loss on classical MSE [81].  
\begin{table}
\begin{tabular}{c c c c} \hline
**Task Type** & **Task Description** & **Semantic Level** & **Typical Papers** \\ \hline Super resolution & Converting image from low resolution & & [14] \\ \cline{2-4} Texture transformation & Converting photos to oil paintings & Low-level & [15] \\ \cline{2-4} Color transformation & Converting grayscale image to color image & & [69] \\ \cline{2-4}  & Coloring in a line drawing & & [33] \\ \hline Environment transformation & Time conversion of landscape photo & & [70] \\ \cline{2-4}  & Season conversion of landscape photo & & [15] \\ \hline Attribute edition & Object style editing & & [38] \\ \cline{2-4}  & Object outline editing & & [33] \\ \cline{2-4}  & Facial attribute editing & & [71] \\ \hline Image synthesis & Converting the semantic mask to a photo & & [72] \\ \cline{2-4}  & Converting sketch to photo & & [73] \\ \cline{2-4}  & Converting normal map to satellite map & & [35] \\ \cline{2-4}  & Converting thermal to photo & & [74] \\ \hline Image inpainting & Repairing the image with some missing pixels & & [75] \\ \hline Data augmentation & Generating more data from existing data & High-level & [66] \\ \hline Image creation & Generating image from text description & & [58] \\ \hline \end{tabular}
\end{table}
Table 4: The classification of tasks involving CGAN.  
Alongside the mentioned papers, numerous papers used CGAN to achieve SR [17, 82, 83, 84]. Some other works on image style transfer mainly focused on the architecture of the CGAN, such as [26, 31, 32, 33, 34, 35, 72, 85]. Recently, a novel model named MSG U-Net architecture is proposed based on [85], where the network is trained by allowing the flow of gradients from multiple-discriminators to a single generator at multiple scales [86].