What is GFG GAN and How can we use it for face restoration?

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GFG GAN (Generative Face Completion and Editing with GAN) is a deep learning model architecture that combines a generative adversarial network (GAN) with an encoder-decoder architecture for face restoration and editing.

The GFG GAN model takes a partially occluded or damaged face image as input and generates a high-quality, restored image as output. The GAN part of the architecture is responsible for generating realistic and high-quality images, while the encoder-decoder part is responsible for filling in missing or damaged parts of the face image.

To use GFG GAN for face restoration, you first need to train the model on a dataset of face images. During the training process, the model learns to generate realistic and high-quality face images from partially occluded or damaged inputs. Once the model is trained, you can use it to restore and edit face images by providing the partially occluded or damaged image as input to the model and obtaining the restored image as output.

In addition to face restoration, GFG GAN can also be used for other face-related tasks, such as face completion, inpainting, and editing.