DuCaGAN: Unified Dual Capsule Generative Adversarial Network for Unsupervised Image-to-Image Translation

With the appearance of Generative Adversarial Network (GAN), image-to-image translation based on a new unified framework has attracted growing interests. As a new technique, it can generate synthesizing images for various requirements in both computer vision and image processing. However, the cycle consistent structure adopted in some common models, such as cycle generative adversarial network (CycleGAN), is usually unable to learn more abundant image features. In this work, we developed a novel model based on GAN, named as dual capsule generative adversarial network (DuCaGAN), by utilizing the distinctive characteristic of view angle invariance and rotation equivariance in capsule network. Firstly, two capsule networks were introduced into the traditional CycleGAN model as discriminators to form our proposed model with six agents. To improve the feature capturing performance, we modified the full objective by combining the margin loss and the original adversarial loss. Furthermore, the Routing Algorithm in the capsule network was optimized by changing its compression function. Finally, experimental results on conventional visual tasks with paired and unpaired datasets demonstrated the superiority and effectiveness of the proposed approach compared to both deep convolutional generative adversarial network (DCGAN) and CycleGAN methods. More importantly, the proposed DuCaGAN was applied for the first time to augment the surface defect data from the real industrial field, and exhibited better performance than those methods available.