A survey on style transfer using generative adversarial networks

Zixuan Wang; Yanshan Wu

doi:10.1117/12.2628465

22 April 2022 A survey on style transfer using generative adversarial networks

Zixuan Wang, Yanshan Wu

Proceedings Volume 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021); 1217416 (2022) https://doi.org/10.1117/12.2628465
Event: International Conference on Internet of Things and Machine Learning (IoTML 2021), 2021, Shanghai, China

Abstract

Developing and investigating recently, a new type of model appeared to greatly help the human with generating models. It can be trained with both supervised or unsupervised learning and contain both generative and discriminative models. Style transfer is one of the functions the GAN can be trained to produce, that it can synthesis two images together to get a new result by having one of the pictures as subject and the other one as style. In this paper, the work will introduce GAN and style transfer in detail and the application of style transfer in the real world.

Citation Download Citation

Zixuan Wang and Yanshan Wu "A survey on style transfer using generative adversarial networks", Proc. SPIE 12174, International Conference on Internet of Things and Machine Learning (IoTML 2021), 1217416 (22 April 2022); https://doi.org/10.1117/12.2628465

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