Conditional generative adversarial networks: introduction and application

Yichen Ding; Siqi Guo

doi:10.1117/12.2641409

10 November 2022 Conditional generative adversarial networks: introduction and application

Yichen Ding, Siqi Guo

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 1234811 (2022) https://doi.org/10.1117/12.2641409
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

Conditional Generative Adversarial Networks (GANs) play an important role in the field of computer vision. Its strength not only lies in its stronger feature extraction ability than traditional algorithms but also has achieved unprecedented success in the mutual game method of two different neural networks. The so-called dual network in GAN is called generator and discriminator. The generator generates a picture close to the expected value by inputting the deep learning network. The discriminator judges whether the image generated by the generator is true. Therefore, the mutual training purpose of the two models is to generate high-quality images infinitely close to the specified features. This paper mainly analyzes the basic network structure of Conditional GAN and its practical application in the fields of image generation, image style transformation, font style transformation, and natural language processing. Different branches of Conditional GAN and optimization methods such as InfoGAN, CycleGAN, StackGAN, McGAN, Conditional SeqGAN are also described. In general, GAN has an unprecedented impact on computer vision and image processing with NLP. At the same time, it also has great potential for future development.

Citation Download Citation

Yichen Ding and Siqi Guo "Conditional generative adversarial networks: introduction and application", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 1234811 (10 November 2022); https://doi.org/10.1117/12.2641409

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KEYWORDS

Gallium nitride

Image processing

Neural networks

Dysprosium

Computer vision technology

Data modeling

Machine vision

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