生成对抗网络及其在纺织行业中的应用

Generative Adversarial Networks and Their Applications in Textile Industry

纺织行业作为国民经济的重要支柱产业,具有体量大、数据量大的特点。如何有效利用数据量大、分类详细、来源不同的纺织数据,成为当前纺织行业数字化转型升级过程中的重要技术议题之一。相对于传统的数字图像处理方法,生成对抗网络(GAN)作为深度学习领域的研究热点,可以根据较少的标注数据,生成多样的高质量数据,能够很好地实现纺织数据样本的增强与生成。从GAN的理论原理出发,分析该模型的优点和缺点;对纺织领域应用最为广泛的6种GAN变体进行介绍;列举GAN在纺织领域中的具体应用现状;对GAN在纺织领域中面临的问题和挑战进行总结。

In comparison to traditional digital image processing methods,the generative adversarial network(GAN),as a research hotspot in the field of deep learning,could generate diverse and high-quality data with minimal labeled data,which could effectively realize the enhancement and generation of textile data samples.Based on the theory of generative adversarial network(GAN),the strengths and weaknesses of the model were analyzed.The six GAN variants widely used in the textile industry were introduced,while the application status of GAN in textile field was also listed.Finally,the challenges and problems faced by GAN in the textile field were summarized.