# Generative Adversarial Network for Overcoming Occlusion in Images: A Survey
## 5 GAN in Amodal Completion
### Amodal Appearance Reconstruction
#### 5.3.2 Face Completion
To minimize the effect of the masked area on the extracted features, two convolutional gating mechanisms are experimented: hard gating mechanism known as partial convolutions [62] and a soft gating method based on sigmoid function. [PERSON] et al. [84] also follow the same approach by implementing a generator (autoencoder), a local discriminator, and a global discriminator. A self-attention mechanism is applied in the global discriminator to enforce complex geometric constrains on the global image structure, and model long-range dependencies. The authors report the results for the facial landmark detection only, without providing the experimental data. Moreover, [PERSON] et al. [85] present FCSR-GAN to create a high-resolution deoccluded image from a low-resolution facial image with partial occlusions. At first, the model is pre-trained for face completion to recover the missing region. Afterward, the entire framework is trained end-to-end. The generator comprises a face completion unit and a face super-resolution unit. The low-resolution occluded input image is fed into the face completion module to fill the missing region. The face completion unit follows an encoder-decoder layout and the overall architecture is similar to the generative face completion by [PERSON] et al. [82]. Then, the occlusion-free image is fed into the face super-resolution module which adopts a SRGAN [86]. The network is trained with a local loss, a global loss, and a perceptual loss to ensure that the generated content is consistent with the local details and holistic contextual information. An additional face parsing loss and perceptual loss are computed to produce more realistic face images. Furthermore, face completion can improve the resistance of face identification and recognition models to occlusion. The authors in [87] propose a two-unit de-occlusion distillation pipeline. In the de-occlusion unit, a GAN is implemented to recover the appearance of pixels covered by the mask. Similar to the previously mentioned works, the output of the generator is evaluated by local and global discriminators. In the distillation unit, a pre-trained face recognition model is employed as a teacher, and its knowledge is used to train the student model to identify masked faces by learning representations for recovered faces with similar clustering behaviors as the original ones. This teaches the student model how to fill in the information gap in appearance space and in identity space. The model is trained with a single occlusion mask at a time; however, in real-world instances, multiple masks cover large discriminative regions of the face. **Multiple generators:** In contrast to the OA-GAN presented by [PERSON] et al. [79], the authors of [88] propose a two-stage OA-GAN framework with two generators and two discriminators. While the generators (\(G_{1}\), and \(G_{2}\)) are made up of a UNet encoder-decoder architecture, PatchGAN is adopted in the discriminators. \(G_{1}\) takes an occluded input image and disentangles the mask of the image to produce a synthesized occlusion. \(G_{2}\) then takes the output from \(G_{1}\) in order to remove the occlusions and generate a deoccluded image. Therefore, the occlusion generator (i.e., \(G_{1}\)) plays a fundamental role in the deocclusion process. The failure in the occlusion generator produces incorrect images. **Multiple generators and discriminators:** While using multiple discriminators ensures the consistency and the validity of the produced image, some available works employ multiple generators, especially when tackling multiple problems. For example, [PERSON] et al. [89] present a framework known as Automatic Mask Generation Network for Face Deocclusion using Stacked GAN (AFD-StackGAN) that is composed of two stages to automatically extract the mask of the occluded area and recover its content. The first stage employs an encoder-decoder in its generator to generate the binary segmentation mask for the invisible region. The produced mask is further refined with erosion and dilation morphological techniques.