关键词: 深度学习, 生成对抗网络（GANs）, 卷积神经网络（CNN）, 图像补全, 跳跃连接

Abstract: In order to repair facial images with large areas damaged efficiently, a method is introduced that using encoder-decoder structured Convolutional Neural Network（CNN） as the generative model, and skip-connections are added between partial layers of it to enhance its structured prediction power, at the same time, an anti-training strategy is introduced to optimize the generative model. This model first trains the discriminator to identify the real image, and then uses the discriminator to judge whether the generator’s output which obtained after the damaged image is input to the generator is true or faker. The discriminator is used to provide gradient to the generator to optimize the generator. Combining the structured prediction power of CNN and the optimization ability of GANs’ antagonism strategy, it improves the results of image completion obviously. The experimental results on the CelebA face dataset show that the proposed method is superior to other methods in repairing images with large areas of damage.

Key words: deep learning, Generative Adversarial Networks（GANs）, Convolutional Neural Network（CNN）, image completion, skip-connection