# Generative Adversarial Networks in Business and Social Science
## 2 Generative Adversarial Networks
### Applications of GANs  
GANs have been employed to address various types of problems:  
* Generating synthetic data for training: In [13; 51], GANs are employed to generate data related to lung cancer patients, and statistical tests are employed to validate such synthetic data. For the generation of time-series data, one can employ SeqGAN [52]. It is also possible to generate synthetic data in tabular datasets using CTGAN [53].
* Generating text and natural language: There are several notable examples: SeqGAN [52], which generates text sequences; LeakGAN [54], which introduces a search policy to enhance text generation; and RankGAN [55]. Others, like textGAN [56], are used for natural language generation. CTRL, proposed by [57], facilitates controlled text generation and enables users to specify style and content. Additionally, GPT-3, one of the most influential studies in text generation using GAN, is based on a transformer architecture that produces high-level, coherent, and natural text [58].
* Generating realistic images: One of the initial applications of GANs for generating realistic images was introduced by [21], where the authors proposed a DCGAN. Subsequently, other authors suggested different GAN modalities for distinct objectives. For instance, [28] applied CycleGAN to generate images by learning domain correspondence without the need for labelled data pairs. Another example is given by [26], which used StackGAN to produce detailed, high-resolution images by introducing a cascaded generator architecture. Other GAN variants, such as those employed for generating human faces [59], include StyleGAN [60; 61; 62] and BigGAN [63]. These models are utilised to enhance the quality and resolution of generated images and employ a scalable architecture. Other DCGAN variants are employed for the generation of human poses from a photograph [64] and can even project the age progression of an individual [65].
* Enhancement and restoration of damaged or low-quality images: To address this issue, several alternatives to the classic GAN have been proposed, such as SRGAN [25], Pix2 Pix [34], and CycleGAN [28]. The latter addresses inpainting tasks (by filling in missing or damaged regions of an image) using DeepFill [66; 67]. To restore images that are corrupted by noise, RedNet was designed [68], and for noise filtering, nCNN and FFDNet were created [69; 70].  
* Speech synthesis: This constitutes one of the earliest application fields of GANs. For instance, WaveGAN was designed for waveform-based voice synthesis [71]. Similarly, MelGAN was developed for voice synthesis using high-quality Mel spectrograms, and it provided enhanced quality and naturalness to synthesised voices [72]. To expedite real-time voice waveform generation, ParallelWaveGAN was proposed [73]. Other generative models include MelGAN-VC, which enables conversation [74], and HiFi-GAN for high-fidelity voice synthesis, which provides greater quality and detail [75; 76]. GANs have also been used for the creation of music and songs by blending the different styles and compositions of recognised musicians and composers [77].  
However, new approaches to apply GANs are constantly emerging, e.g., in anomaly detection [78], data imbalance problems [79], probabilistic forecasting of financial time series [45], stock price prediction [80], etc.