Face Image Inpainting With Evolutionary Generators

Abstract

Recently, deep learning has become a mainstream method of image inpainting. It can not only restore the image texture, obtain high-level abstract features of images, but also restore semantic images such as human face images. Among these methods, generative adversarial networks (GANs) using autoencoder as the generator have become the promising model for image inpainting. These models implement the end-to-end image inpainting and also generate visually reasonable and clear image structures and textures. However, GANs often have problems with gradient vanishing and model collapse during training, so we propose a Generative Adversarial Network with Evolutionary Generators (EG-GAN) and apply it in face image inpainting. To stabilize the model training process, EG-GAN trains the generator network by evolution, combines two mutation functions as a training objective to update the parameter of generator networks, and produces offspring generators through crossover, using the matcher assists the discriminator to criticize the generated image. Experiments on various face image datasets such as CelebA-HQ and CelebA show that EG-GAN successfully overcomes the gradient vanishing problem, achieves stable and efficient training, and generates visually reasonable images.