SE-GAN: A Swap Ensemble GAN Framework

In recent years, Generative adversial network(GAN) becomes prevalent in the research and was applied in various fields. Though GAN attracting great attention undoubtedly, it was still blamed for the difficulty of training. Training a GAN is very likely to get into undesirable results such as gradient vanishing, gradient explosion, model collapse. In order to train GAN more efficiently, this study proposes the Swap Ensemble Generative Adversial Network(SE-GAN), a framework for training an ensemble GAN. Based on the concept of information sharing, SE-GAN allows serveral GANs to be trained in parallel: one of them is the leader, while the others are the workers. The leader pair swaps with each worker periodically to collect the information of all workers. At the same time, each worker acquires the information the leader learned. Under such implementation, the leader and workers would help each other.In this paper, a toy dataset was used to describe the concept and better show the effect of swap ensemble framework. And three real-world datasets were used to validate the effect. Results of the experiments shows this framework improves the performance and efficiency in training GAN.