The critical challenge of image inpainting is to infer reasonable semantics and textures for a corrupted image. Typical methods for image inpainting are built upon some prior knowledge to synthesize the complete image. One potential limitation is that those methods often remain undesired blurriness or semantic mistakes in the synthesized image while handling images with large corrupted areas. In this paper, we propose a Collaborative Contrastive Learning-based Generative Model (C2LGM), which learns the content consistency in the same image to e...
