# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Training Data  
Supervised learning frameworks require annotated ground-truth data to train a model. These data can be either from a manually annotated dataset, a synthetic occluded data from 3D computer-generated images, or by superimposing a part of an object/image on another object. For example, [PERSON] et al. [76] train their model (SeGAN) on a photo-realistic synthetic dataset, and [PERSON] et al. [75] apply a self-supervised approach to generate annotated training data. However, a model trained with synthetic data may fail when it is tested on real-world data, and human-labeled data are costly, time-consuming, and susceptible to subjective judgments. In this section, we discuss how GAN is implemented to generate training data for several categories. **Generic objects:** It is nearly impossible to cover all the probable occlusions, and the likelihood of appearance of some occlusion cases is rather small. Therefore, [PERSON] et al. [115] aim to utilize the data to improve the performance of the object detection in the case of occlusions. They utilize an adversarial network to generate hard examples with occlusions, and use them to train a Fast-RCNN [116]. Consequently, the detector becomes invariant to occlusions and deformations. Their model contains an Adversarial Spatial Dropout Network (ASDN), which takes as input features from an image patch and predicts a dropout mask that is used to create occlusion such that it would be difficult for Fast-RCNN to classify. Likewise, [PERSON] et al. [117] apply an adversarial network to produce occluded adversary samples to train an object detector. The model, named Feature Fusion and Adversary Networks (FFAN), is based on Faster RCNN [118] and consists of a feature fusion network and an adversary occlusion network, and while the feature fusion module produces a feature map of high resolution and high semantic information to detect small objects more effectively, the adversary occlusion module produces occlusion on the feature map of the object thus outputs an adversary training sample that would be hard for the detector to discriminate. Meanwhile, the detector becomes better in classifying the generated occluded adversary samples through self-learning. Over time, the detector and the adversary occlusion network learn and compete with each other to enhance the performance of the model. The occlusions produced by adversary networks in [115; 117] may lead to over-generalization, because they are similar to other class instances. For example, the occluded wheels of a bicycle results in misclassifying a wheel chair as a bike. **Humans:** [PERSON] et al. [119] augment the input data to produce easy-to-hard occluded samples with different sizes and positions of the occlusion mask to increase the variation of occlusion patterns. They address the issue of ReID under occlusion through an Incremental Generative Occlusion Adversarial Suppression (IGOAS) framework. The network contains two modules, an incremental generative occlusion (IGO) block, and a global adversarial suppression (G&A) module. IGO takes the input data through augmentation and generates easy occluded samples. Then, it progressively enlarges the size of the occlusion mask with the number of training iterations. Thus, the model becomes more robust against occlusion as it learns harder occlusion incrementally rather than hardest ones directly. On the other hand, G&A consists of a global branch which extracts global features of the input data, and an adversarial suppression branch that weakens the response of the occluded region to zero and strengthens the response to non-occluded areas. Furthermore, to increase the number of samples per identity for person ReID, [PERSON] et al. [120] use a GAN network to synthesize labeled occluded data. Specifically, the authors impose block rectangles on the images to create random occlusion on the original person images which the model then tries to complete.