## 2 Related work
### Generative Adversarial Networks (GANs)  
Recently, several GAN-based methods have been proposed and have achieved great success in various computer vision related applications, such as image-to-image translation [14; 15], super-resolution [16; 17], image inpainting [18; 19], and text to image [20; 21; 22]. For the image-to-image translation topic, [PERSON] et al. in [14] investigated conditional adversarial networks for image-to-image translation problems, and they demonstrated that this approach was effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. [PERSON] et al. [15] proposed a principled Perceptual Adversarial Networks (PAN) for image-to-image transformation tasks. The proposed PAN consisted of two feed-forward convolutional neural networks (CNNs): the image transformation network T and the discriminative network D. They achieved quite good image-to-image translation results. On the super-resolution topic, [16] presented a novel deep learning approach for single image super-resolution (SR). It was designed to learn the mapping between low and high-resolution images. Some extra pre-processing and post-processing were also conducted. It had a lightweight structure, yet demonstrated state-of-the-art restoration quality and achieved fast speed for practical on-line usage. Meanwhile, [17] described a deep residual network SRResNet that demonstrated a new state-of-the-art performance on public benchmark datasets when evaluated with the widely used PSNR measure. For image inpainting, [PERSON] in [18] presented Context Encoders--a convolutional neural network trained to generate the contents of an arbitrary image region conditioned on its surroundings. Through experiments, they found that a context encoder captured not just appearance but also the semantics of visual structures. Also, [PERSON] [19] proposed an effective face completion algorithm using a deep generative model; it was trained with a combination of a reconstruction loss, two adversarial losses and a semantic parsing loss, which ensured pixel faithfulness and local-global contents consistency. The results demonstrated qualitatively and quantitatively that the model was able to deal with a large area of missing pixels. Finally, in the text to image translation topic, [PERSON] et al. in [21] presented the Generative Adversarial What-Where Network (GAWWN) that synthesized images given instructions describing what content to draw in which location. The model also enabled conditioning on arbitrary subsets of parts, and yielded an efficient interface for picking part locations. [PERSON] et al. [22] proposed Stacked Generative Adversarial Networks (StackGANs) aimed at generating high-resolution photo-realistic images. It included multiple generators and multiple discriminators arranged in a tree-like structure, that is, images at multiple scales corresponding to the same scene are generated from different branches of the tree. Extensive experiments demonstrated that the proposed stacked generative adversarial networks significantly outperformed other state-of-the-art methods in generating photo-realistic images. One of the biggest challenges GANs and other research topics have to deal with is the imbalance scenario, as the number of samples belonging to one class is remarkably lower than those belonging to the other classes. There are different approaches to solving the imbalanced issue such as resampling (i.e., trying to get an equal number of samples for both classes by increasing the frequency of the minority class and reducing the frequency of the majority class. ), or ensemble (enhancing the classifier's performance by building several two-stage classifiers from the original data and then adding up their predictions). For the task of generating CG image, GANs have achieved quite impressive performance because they can generate images that look realistic.