# Contextual Attention > we propose a new deep generative model-based approach which can not only **synthesize novel image structures** but also **explicitly utilize surrounding image features as references** during network training to make better predictions. ### features 1. multiple holes at arbitrary locations 2. variable sizes ### Purpose Traditional method can only be used in background generation. However, this method can generate something that **can't be copied from the image**. For example, faces and objects. ## Core Tech ### CNN CNN-based methods often create **boundary artifacts, distorted structures and blurry textures inconsistent with surrounding areas.** ### GAN(WGAN) The whole network is trained end to end with reconstruction losses and two Wasserstein GAN losses [1, 13], where one critic looks at the global image while the other looks at the local patch of the missing region. ### Keypoints We propose a novel contextual attention layer to explicitly attend on related feature patches at distant spatial locations.