Optoelectronic generative adversarial networks
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866916692506968064 |
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| author | Qiu, Jumin Lu, Ganqing Liu, Tingting Zhang, Dejian Xiao, Shuyuan Yu, Tianbao |
| author_facet | Qiu, Jumin Lu, Ganqing Liu, Tingting Zhang, Dejian Xiao, Shuyuan Yu, Tianbao |
| contents | Artificial intelligence generative content technology has experienced remarkable breakthroughs in recent years and is quietly leading a profound transformation. Diffractive optical networks provide a promising solution for implementing generative model with high-speed and low-power consumption. In this work, we present the implementation of a generative model on the optoelectronic computing architecture, based on generative adversarial network, which is called optoelectronic generative adversarial network. The network strategically distributes the generator and discriminator across the optical and electronic components, which are seamlessly integrated to leverage the unique strengths of each computing paradigm and take advantage of transfer learning. The network can efficiently and high-speed process the complex tasks involved in the training and inference of the generative model. The superior performance of these networks is verified by engaging three types of generative tasks, image generation, conditional generation, and image restoration. By synergistically combining the strengths of optical and electronic computing, the optoelectronic generative adversarial network paves the way for the development of more powerful and accessible artificial intelligence generative content technology that can unlock new creative possibilities across a wide range of applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_16672 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Optoelectronic generative adversarial networks Qiu, Jumin Lu, Ganqing Liu, Tingting Zhang, Dejian Xiao, Shuyuan Yu, Tianbao Optics Artificial intelligence generative content technology has experienced remarkable breakthroughs in recent years and is quietly leading a profound transformation. Diffractive optical networks provide a promising solution for implementing generative model with high-speed and low-power consumption. In this work, we present the implementation of a generative model on the optoelectronic computing architecture, based on generative adversarial network, which is called optoelectronic generative adversarial network. The network strategically distributes the generator and discriminator across the optical and electronic components, which are seamlessly integrated to leverage the unique strengths of each computing paradigm and take advantage of transfer learning. The network can efficiently and high-speed process the complex tasks involved in the training and inference of the generative model. The superior performance of these networks is verified by engaging three types of generative tasks, image generation, conditional generation, and image restoration. By synergistically combining the strengths of optical and electronic computing, the optoelectronic generative adversarial network paves the way for the development of more powerful and accessible artificial intelligence generative content technology that can unlock new creative possibilities across a wide range of applications. |
| title | Optoelectronic generative adversarial networks |
| topic | Optics |
| url | https://arxiv.org/abs/2412.16672 |