Generative Image Inpainting

Recently, image is becoming more and more important as a carrier of information, and the demand of image inpainting is increasing. We present an approach for image inpainting in this paper. The completion model contains one generator and double discriminators. The generator is the architecture of AutoEncoders with skip connection and the discriminators are simple convolutional neural networks architecture. Wasserstein GAN loss is used to ensure our model’s stable training. We also give the algorithm of training our model in this paper.