无监督训练:在哪里不对称的对比学习遇到自我相似性
IEEE transactions on pattern analysis and machine intelligence
|October 2, 2023
概括
本研究介绍了一种无监督非局部对比学习 (NLCL) 方法,用于脱轨,克服合成数据中的域差距. 它利用层内部的相似性和层间的不相似性来改善从真实图像中去除雨水.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 图像处理 图像处理
背景情况:
- 由于域间的差距,监督脱轨方法在与真实世界的数据中扎.
- 现有的方法往往独立处理图像和雨层,忽视它们之间的关系.
研究的目的:
- 开发一种无人监督的脱轨方法,可以更好地将其推广到现实世界的雨景.
- 解决当前脱轨技术中合成训练数据和独立层处理的局限性.
主要方法:
- 提出了一个无监督的非本地对比学习 (NLCL) 脱轨方法.
- 利用非局部自我相似性补丁利用层内相似性和层间不相似性.
- 引入了基于图像和雨块之间的维度差异的不对称对比损失.
- 收集了一个大规模的,高分辨率的真实世界雨水数据集.
主要成果:
- 通过学习紧的表示和歧视性分解,NLCL方法有效地将雨水与清洁图像区分开来.
- 不对称的损失通过建模紧度差异来增强分解.
- 在各种现实雨天数据集上展示了最先进的性能.
结论:
- 无监督的对比学习有效地弥合了脱轨的领域差距.
- 利用层内部的相似性和层间的不相似性对于强大的脱轨至关重要.
- 提出的方法为现实世界的图像脱轨应用提供了显著的进步.
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