# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 4 Background  
### Generative Adversarial Network  
GAN is an unsupervised generative model that contains two networks, namely a generator and a discriminator. The two networks learn in an adversary manner similar to the min-max game between two players. The generator tries to generate a fake sample that the discriminator cannot distinguish from the real sample. On the other hand, the discriminator learns to determine whether the sample is real data or generated. The generator \(G\) takes a random noise \(z\) as input. It learns a probability distribution \(p_{g}\) over \begin{table}
\begin{tabular}{l l l} \hline \hline \# & **Title** & **Pub.** & **Year** \\ \hline
1 & Generative adversarial networks: introduction and outlook [37] & IEEE & 2017 \\
2 & Comparative study on generative adversarial networks [51] & arXiv & 2018 \\
3 & Generative adversarial networks: An overview [52] & IEEE & 2018 \\
4 & Recent progress on generative adversarial networks (GANs): A survey [34] & IEEE & 2019 \\
5 & How generative adversarial networks and their variants work: An overview [32] & ACM & 2019 \\
6 & Generative adversarial networks (GANs): An overview of theoretical model, evaluation metrics, and recent developments [35] & arXiv & 2020 \\
7 & Generative adversarial network technologies and applications in computer vision [36] & Hindawi & 2020 \\
8 & Generative adversarial networks in digital pathology: a survey on trends and future potential [42] & Elsevier & 2020 \\
9 & Deep generative adversarial networks for image-to-image translation: A review [38] & MDPI & 2020 \\
10 & A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications [50] & IEEE & 2021 \\
11 & Generative adversarial network: An overview of theory and applications [49] & Elsevier & 2021 \\
12 & The theoretical research of generative adversarial networks: an overview [33] & 2021 \\
13 & Generative adversarial networks (GANs) challenges, solutions, and future directions [28] & ACM & 2021 \\
14 & Generative adversarial networks: a survey on applications and challenges [31] & Springer & 2021 \\
15 & A survey on generative adversarial networks for imbalance problems in computer vision tasks [46] & Springer & 2021 \\
16 & Generative Adversarial Networks and their Application to 3D Face Generation: A Survey [41] & Elsevier & 2021 \\
17 & Applications of generative adversarial networks (GANs): An updated review [45] & Springer & 2021 \\
18 & Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy [3] & ACM & 2022 \\
19 & Exploring Generative Adversarial Networks and Adversarial Training [27] & Elsevier & 2022 \\
20 & Generative Adversarial Networks for face generation: A survey [40] & ACM & 2022 \\
21 & Generative Adversarial Networks: A Survey on Training, Variants, and Applications [30] & Springer & 2022 \\
22 & Augmenting data with generative adversarial networks: An overview [47] & IOS & 2022 \\
23 & A Survey on Training Challenges in Generative Adversarial Networks for Biomedical Image Analysis [43] & arXiv & 2022 \\
24 & Attention-based generative adversarial network in medical imaging: A narrative review [44] & Elsevier & 2022 \\
25 & Generative adversarial networks for image super-resolution: A survey [48] & arXiv & 2022 \\
26 & Generic image application using GANs (Generative Adversarial Networks): A Review [39] & Springer & 2022 \\
27 & A Survey on Generative Adversarial Networks: Variants, Applications, and Training [29] & ACM & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Available survey articles about GAN that were published between 2017 and 2022.  
data \(x\) to generate fake samples that imitate the real data distribution (\(p_{data}\)). Then, the generated sample is forwarded to the discriminator \(D\) which outputs a single scalar that labels the data as real or fake (Figure 1). The classification result is used in training \(G\) as gradients of the loss. The loss guides \(G\) to generate samples that are less likely and more challenging to be labeled as fake by the \(D\).