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

中文翻译：

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计算机视觉中的生成对抗网络技术和应用。


计算机视觉是深度学习最热门的研究领域之一。生成对抗网络（GAN）的出现为计算机视觉提供了新的方法和模型。 GANs采用游戏训练方式的思想在特征学习和图像生成方面优于传统的机器学习算法。 GAN 不仅广泛应用于图像生成和风格迁移，还广泛应用于文本、语音、视频处理等领域。然而，GAN仍然存在一些问题，例如模型崩溃和训练不可控等。本文深入回顾了 GAN 的理论基础，并对一些最近开发的 GAN 模型进行了调查，并与传统的 GAN 模型进行了比较。 GAN 在计算机视觉中的应用包括数据增强、域传输、高质量样本生成和图像恢复。介绍了GANs在基于人工智能（AI）的安全攻防方面的最新研究进展。论文最后还讨论了 GAN 在计算机视觉中的未来发展以及人工智能在计算机视觉中的可能应用。