Transformer-based conditional generative adversarial networks for image generation

Haofei Xi; Huafeng Qin; Zhipeng Xiong; Jiayuan Zhang

doi:10.1117/12.2645512

23 August 2022 Transformer-based conditional generative adversarial networks for image generation

Haofei Xi, Huafeng Qin, Zhipeng Xiong, Jiayuan Zhang

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Proceedings Volume 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022); 1230513 (2022) https://doi.org/10.1117/12.2645512
Event: International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 2022, Hangzhou, China

Abstract

The recent transformers shows competitive performance on computer vision tasks, such as classification, detection, and segmentation. Inspire by its success, in this paper, we explore its application at some more notoriously difficult vision tasks such conditional generative adversarial networks and propose a transformer based conditional generative adversarial networks for image generation. Our model employs the transformer architecture to develop a generator discriminator and residual network as discriminator. To improve the performance, a spectral normalization technique is used to normalize the weights of discriminator and the hinge loss are determined for model optimization. The experimental results on four public datasets shows that our approach is capable of producing the high quality images with good consistence and diversity and outperforms existing works.

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Haofei Xi, Huafeng Qin, Zhipeng Xiong, and Jiayuan Zhang "Transformer-based conditional generative adversarial networks for image generation", Proc. SPIE 12305, International Symposium on Artificial Intelligence Control and Application Technology (AICAT 2022), 1230513 (23 August 2022); https://doi.org/10.1117/12.2645512

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KEYWORDS

Transformers

Data modeling

Image quality

Image processing

Gallium nitride

Performance modeling

Data processing

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