基于强度的投影层作为有效的非线性激活的光学神经网络
Jiawei Xi1, Randy Stefan Tanuwijaya1, Tan Li2
1Department of Physics, The Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, China.
Scientific reports
|December 9, 2025
概括
我们引入了使用光强度测量的投影层,作为光学神经网络 (ONN) 的新型非线性激活函数. 这种方法提高了深度学习应用中的速度和能源效率.
科学领域:
- 光学是什么?光学是什么?光学是什么?
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 光学神经网络 (ONN) 通过利用光的并行性来实现高速和能源效率.
- 在ONN中实现非线性激活函数仍然是一个重要的设计挑战.
- 现有的方法往往缺乏物理可实现性或硬件效率.
研究的目的:
- 提出和验证投影层作为ONN有效的非线性激活函数.
- 为了证明这种新方法的稳定性和可扩展性.
- 将该方法扩展到四次数值表示,用于多维特征提取.
主要方法:
- 使用光学强度测量作为从复杂/四值场到实值大小的投影.
- 使用振幅或相位编码来将信号传输到下一层.
- 在图像分类,重建和物理参数推断任务中进行系统评估.
主要成果:
- 投射层有效地作为ONN中的非线性激活机制.
- 该方法在噪音条件下表现出稳健性.
- 在β-变量自编码器 (β-VAE) 中成功应用,用于可解释的物理参数学习.
结论:
- 投影层为深度ONN提供了一个可扩展和硬件高效的基础.
- 这种方法利用光学计算的优势来提高性能.
- 它在图像识别,信号处理和科学推理方面具有广泛的应用性.
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