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摘要
In tasks of unsupervised learning, the generative model is one of the most critical techniques. The generative model consists of probability density estimation and sampling, which can learn data distribution by looking at existing samples and generate new samples that obey the same distribution as the original samples. For complex distributions in a high dimensional space, density estimation and sample generation are often hard to realize. Since high-dimensional random vectors are generally difficult to model directly, it is necessary to simplify the model with some condition independence hypothesis. Even given a complex distribution that has been modeled, there is a lack of effective sampling methods. With the rapid development of deep neural network technology, the generative model has made great progress. In the past few years, there has been a drastic growth of research in Generative Adversarial Network (GAN) which can model an unknown distribution in an indirect way and can avoid statistical and computational challenges. At the same time, generative adversarial networks are the latest and most successful technology among generative models. Especially in terms of image generation, compared with other generation models, generative adversarial networks can not only avoid complicated calculations, but also generate better quality images. Therefore, this paper will make a summary and analysis of generative adversarial networks and its applications in image generation. Firstly, from the theoretical aspect, the basic idea and working mechanism of generative adversarial networks are explained in detail; How to design the loss function of generative adversarial networks based on F-divergence or integral probability metric is introduced, and its advantages and disadvantages are summarized; From the two aspects of convolutional neural network structure and auto-encoder neural network structure, the model structure commonly used in generating adversarial networks is summarized; At the same time, the problems and corresponding solutions in the process of training generative adversarial networks are analyzed from both theoretical and practical perspectives; Secondly, based on the direct method and the integration method as the classification criteria, current methods of generating images based on generating adversarial networks are summarized, and the basic ideas of these methods are explained in details. Then, from the three aspects of image generation based on mutual information, image generation based on attention mechanism, and image generation based on a single image, the method of directly generating images based on random noise vectors is summarized. The current methods of generating images based on image translation are explained in details from the aspects of supervised and unsupervised methods. Later, from a qualitative and quantitative point of view, the existing methods used to evaluate the quality and diversity of generated images based on generative adversarial networks are analyzed, and contrasted. Finally, the application of generative adversarial networks in the field of small samples, data category imbalance, target detection and tracking, image attribute editing, and medical images processing is introduced in details. And some problems in theory and practice of generative adversarial networks and image generation are analyzed; The development trend of generative adversarial networks and the development trend of image generation are summarized and prospected.
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