Regional attention generative adversarial network

Abstract

In this Letter, the authors propose a novel attention mechanism combined with a classical generative adversarial network (GAN) model to improve the visual quality of generated samples. This novel attention model is named regional attention GAN. The proposed mechanism can build dependencies between the high-level representations extracted from attention regions of real images and corresponding feature maps of the generative network. By modelling these dependencies, the generative network can be facilitated to learn feature mapping and fit the distribution of real data. They conduct extensive experiments on widely used datasets CIFAR-10, STL-10, and CelebA. The quantitative and qualitative performance improvement over state-of-the-art methods demonstrates the validity of the proposed attention mechanism in improving the quality of generated images.