Abstract: ‍ ‍Generative adversarial network is composed of generative model and discriminant model. Generative model mainly obtains the probability distribution of real data， and the discriminant model is used to judge whether the input is real data or the data generated by the generator. Through the training of the two models against each other， the generative model can finally learn the distribution of real data completely. It also makes it impossible for the discriminant model to accurately determine whether the input data comes from the generating model or the discriminant model. At the same time， it opened up a new way of thinking for the improvement of algorithm performance in visual classification tasks. Since its inception， there have been a lot of variations in a variety of related fields. The main contents of this paper include： （1） We gave a brief introduction about the current research status of generative adversarial network and application scenarios， and further describe the basic model architecture and the drawbacks of generative adversarial network itself. （2） We presented the development and improved methods of generative adversarial network from three aspects： network architecture， loss function and training mode. （3） We typically described a variety of applications of generative adversarial network， as well as the advantages and disadvantages of these algorithms. These typical applications included face image generation and editing， style transfer， image super resolution， image restoration， sequence data generation， video generation and other application fields. （4） The common evaluation indexes for generative adversarial networks were introduced and the applicable scenarios and shortcomings of these indexes were analyzed. （5） Finally， we discussed and presented the challenges of generative adversarial network from several representative aspects， and further we pointed out the possible improvement directions in the future.