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结用于denoiser的编码器的预训练参数:扩大像素参与度和过高频噪声
Jie Zhang1, Junda Li1, Liang Feng2
1Advanced Materials Thrust, The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 51140, China.
概括
研究人员开发了一种简单的训练方法,以改善深度学习. 在训练解码器时结编码器参数可以提高对各种噪音类型的概括性,从而提高无声化性能.
科学领域:
- 计算机视觉 计算机视觉
- 深度学习 (Deep Learning) 是一种深度学习.
- 图像处理 图像处理
背景情况:
- 在深度学习中,图像无声化至关重要,但与分布外噪声泛化作斗争.
- 模型概括依赖于架构,数据集和培训方法,培训方法被探索不足.
研究的目的:
- 提出和验证一个简单的培训策略,以提高深度学习的概括能力.
- 解决关于培训方法的研究缺口,以改善denoiser性能.
主要方法:
- 建议结预先训练的编码器参数,同时在训练过程中更新解码器参数.
- 在各种噪音类型中验证了这项培训策略的有效性.
主要成果:
- 拟议的方法显著提高了对不同类型噪声的消噪器稳定性和一般化性能.
- 结编码器扩大了输入像素的影响,并有效地过了高频噪声信号.
结论:
- 简单的结编码器的策略增强了对图像删除任务的网络概括性.
- 这种方法提供了一种新且有效的方法,可以在不需要复杂的架构更改的情况下提高消毒器的性能.
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