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.

中文翻译：

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用进化生成器修复人脸图像

最近，深度学习已成为图像修复的主流方法。它不仅可以还原图像纹理，获得图像的高级抽象特征，还可以还原语义图像，例如人脸图像。在这些方法中，使用自动编码器作为生成器的生成对抗网络（GAN）已成为有希望的图像修复模型。这些模型实现了端到端的图像修复，还生成了视觉上合理且清晰的图像结构和纹理。但是，GAN在训练过程中经常会出现梯度消失和模型崩溃的问题，因此我们提出了一种带有进化生成器的生成对抗网络（EG-GAN），并将其应用于人脸图像修复。为了稳定模型训练过程，EG-GAN通过进化来训练发电机网络，结合两个变异函数作为训练目标以更新生成器网络的参数，并通过交叉生成后代生成器，使用匹配器帮助鉴别器批评生成的图像。在CelebA-HQ和CelebA等各种面部图像数据集上进行的实验表明，EG-GAN成功克服了梯度消失的问题，实现了稳定有效的训练，并生成了视觉上合理的图像。