Persistent Homology Based Generative Adversarial Network

Jinri Bao, Zicong Wang, Junli Wang, Chungang Yan

Abstract: In recent years, image generation has become one of the most popular research areas in the field of computer vision. Significant progress has been made in image generation based on generative adversarial network (GAN). However, the existing generative models fail to capture enough global structural information, which makes it difficult to coordinate the global structural features and local detail features during image generation. This paper proposes the Persistent Homology based Generative Adversarial Network (PHGAN). A topological feature transformation algorithm is designed based on the persistent homology method and then the topological features are integrated into the discriminator of GAN through the fully connected layer module and the self-attention module, so that the PHGAN has an excellent ability to capture global structural information and improves the generation performance of the model. We conduct an experimental evaluation of the PHGAN on the CIFAR10 dataset and the STL1