Computer vision is one of the hottest research fields in deep learning. The emergence of generative adversarial networks (GANs)\nprovides a new method and model for computer vision. The idea of GANs using the game training method is superior to\ntraditional machine learning algorithms in terms of feature learning and image generation. GANs are widely used not only in\nimage generation and style transfer but also in the text, voice, video processing, and other fields. However, there are still some\nproblems with GANs, such as model collapse and uncontrollable training. This paper deeply reviews the theoretical basis of GANs\nand surveys some recently developed GAN models, in comparison with traditional GAN models. The applications of GANs in\ncomputer vision include data enhancement, domain transfer, high-quality sample generation, and image restoration. The latest\nresearch progress of GANs in artificial intelligence (AI) based security attack and defense is introduced. Thefuture development of\nGANs in computer vision is also discussed at the end of the paper with possible applications of AI in computer vision.
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