# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 3 Related Works  
**Occlusion:** Handling occlusion has been studied in various domains and applications. Table 1 shows the list of published surveys and reviews of occlusion in several applications. A survey of occlusion handling in generic object detection of still images is provided in [2], focusing on challenges that arise when objects are occluded. Similarly, the most recent survey article by the authors of [13] provides the taxonomy of problems in amodal completion from single 2D images. However, none of those review articles concentrate on the applications of GAN for overcoming occlusion particularly. Other works have focused on occlusion in specific scopes, such as object tracking [14; 15], pedestrians [16; 17], human faces [18; 19; 20; 21; 22], automotive environment [23; 24], and augmented reality [25]. In contrary, we review the articles that address occlusion in single 2D images. **Generative Adversarial Network:** Due to their power, GANs are ubiquitous in computer vision research. Due to the growing body of published works in GAN, there are several recent surveys and review papers in the literature investigating its challenges, variants, and applications. Table 2 contains a list of survey articles that have been published in the last five years. The list does not include papers that specifically focus on GAN applications outside the computer vision field. The authors in [27; 28; 29; 30; 31; 32] discuss the instability problem of GAN with the various techniques and improvisations that have been designed to stabilize its training. Adversarial attack can be carried out against machine learning models by generating an input sample that leads to unexpected and undesired results by the model. [PERSON] et al. [27] investigate the various defense mechanisms to protect GAN against such attacks. [PERSON] et al. [33] summarize the different models into two groups of GAN architectures: the two-network \begin{table}
\begin{tabular}{l l l l} \hline \hline \# & **Title** & & **Pub.** & **Year** \\ \hline
1 & Multiple camera based multiple object tracking under occlusion: A survey [14] & IEEE & 2017 \\
2 & Facial expression analysis under partial occlusion: A survey [18] & ACM & 2018 \\
3 & Occlusion detection and restoration techniques for 3D face recognition: a literature review [19] & Springer & 2018 \\
4 & Overcoming occlusion in the automotive environment—A review [23] & IEEE & 2019 \\
5 & A comprehensive survey on multi object tracking under occlusion in aerial image sequences [15] & IEEE & 2019 \\
6 & A Survey on Occluded Face recognition [26] & ACM & 2020 \\
7 & Occlusion Handling in Generic Object Detection: A Review [2] & IEEE & 2021 \\
8 & Occlusion Handling in Augmented Reality: Past, Present and Future [25] & IEEE & 2021 \\
9 & A survey of face recognition techniques under occlusion [20] & Wiley & 2021 \\
10 & Survey of pedestrian detection with occlusion [16] & Springer & 2021 \\
11 & Occlusion Handling and Multi-scale Pedestrian Detection Based on Deep Learning: A Review [17] & IEEE & 2022 \\
12 & Image Amodal Completion: A Survey [13] & arXiv & 2022 \\
13 & A Literature Survey of Face Recognition Under Different Occlusion Conditions [21] & IEEE & 2022 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Existing survey articles about occlusion that were published between 2017 and 2022.  
models and the hybrid models, which are GANs combined with an encoder, autoencoder, or variational autoencoder (VAE) to enhance the training stability. The authors of [34; 35] explore the available evaluation metrics of GAN models. Other works have discussed the application of different GAN architectures for computer vision [36; 37], image-to-image translation [38; 39], face generation [40; 41], medical field [29; 42; 43; 44], person re-identification (ReID) [45], audio and video domains [29], generating and augmenting training data [46; 47], image super-resolution [39; 48], and other real-world applications [39; 45; 49; 50].