# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Training Data
The completed images that are similar but not identical to the original input are labeled with the same annotation as the corresponding raw image. Similarly, [PERSON] et al. [113] follow the same strategy to expand the original training set, expect that an additional noise channel is applied on the generated data to adjust the label further. Both approaches in [113; 120] work with rectangular masks, but in real-world examples occlusions appear in free-form shapes. **Face images:** [PERSON] and [PERSON] [106] propose an improved GAN to generate occluded face images. The model is based on DCGAN with an added S-coder. The purpose of the S-coder is to force the generator to produce multi-class target images. The network is further optimized through Wasserstein distance and the cycle consistency loss from CycleGAN. However, only sunglasses and facial masks are considered as occlusive elements. Figure 4 outlines of the discussed approaches for tackling the issues in overcoming occlusion through using GAN. Table 4 summarizes the GAN model, the loss function, and the datasets that were used in the discussed works in this section (except for the face completion works), it also shows the reported result for the tasks where GAN was applied. Figure 4: Outline of the approaches for addressing the challenges in overcoming occlusion through GAN. For amodal segmentation the implemented architecture are, a discriminator with a two hourglass generator [60], a coarse-to-fine architecture with contextual attention [63] or multiple discriminators [67], and a generator with priori knowledge [66]. For order recovery, GAN is designed as a generator with a single discriminator [71; 73], or multiple discriminators [69; 70]. To perform amodal content completion for facial images, the architectures include: a single generator and discriminator [79; 80; 81], multiple discriminators [82; 83; 84; 85; 86; 87], multiple generators [88], multiple generators and discriminators [89; 89], or a coarse-to-fine architecture [91; 92; 93]. Generic object completion is carried out through coarse-to-fine architecture [63], multiple discriminators with contextual attention [78], or partial convolution and CGAN [75; 76]. Human completion for attribute classification is utilized in [108; 110]. Other works use GAN to complete the images of food [112], vehicles [67], and humans [66; 114]. GAN is also used to generate training data of generic objects [115; 117], humans [113; 119; 120], and face images [106]. \begin{table}
\begin{tabular}{p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt} p{42.7 pt}} \hline \hline
**\#** & **Paper** & **Model** & **Loss Function** & **Dataset** & **Task** & **Results** \\ \hline
11 & [PERSON] et al. [15] & Inpainter with contextual attention loss & Spatially discounted recommendation \(\mathcal{L}\) loss, local and global WLAN-CP driver results & COCO-Stuff [13] & Content completion [31] & User study positive = 79.7\%, negative = 20.3\% \\ \hline
12 & [PERSON] et al. [10] & DCCAN-based & 1. For attribute classification: weighted BCI loss. 2. For content completion research loss and adversarial loss of the generative. & RAP [13] & Content completion & mA = 65.82, accuracy = 76.01, P + 84.98, P + 85.80, P + 85.0 & RAF = 71.23 & Context completion & mA = 65.82, accuracy = 76