通过利用前景本地化中的噪音引起的扭曲来促进噪音标签学习
1College of Computer Science and Software Engineering, Hohai University, Nanjing 211100, China.
概括
本研究介绍了FLSC,这是一个新的框架,它使用前景本地化来改进使用噪音标签的深度学习模型培训. FLSC 增强了样本选择和标签校正,以实现更强大的模型通用化.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 深度神经网络需要大量的,注释良好的数据集来进行有效的训练.
- 数据集中的噪音标签可以显著损害模型概括.
- 目前用于噪声标签减轻的方法通常依赖于损失或信心分布.
研究的目的:
- 用空间注意力研究标签噪声对前景定位的影响.
- 提出一个新的框架,FLSC,强大的噪音标签学习.
- 在有噪音数据的情况下,提高样本选择和标签校正的准确性.
主要方法:
- 分析视觉激活地图上的空间注意力分布,以检测噪音引起的扭曲.
- 提出一个两阶段框架 (FLSC),将前景本地化与噪音标签学习相结合.
- 实施适应噪音的对抗性删除策略,以抑制背景激活和改善表示学习.
主要成果:
- 噪音标签会导致模型注意无关的背景区域或对象边缘.
- 为了更好的样本选择,FLSC量化了前景定位中的噪声引起的扭曲.
- 在合成和现实世界的数据集上,FLSC在最先进的方法上表现出卓越的性能.
结论:
- 基于功能激活来评估本地化质量是一种解决标签噪音的新方法.
- FLSC有效地减轻了噪音标签对模型培训的负面影响.
- 拟议的方法增强了深度学习模型的稳定性和通用性.
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