对于联合偏见的渐变意识学习:标签噪音和阶级失衡
Shichuan Zhang1, Chenglu Zhu2, Honglin Li1
1Zhejiang University, Hangzhou, Zhejiang 310027, China; School of Engineering, Westlake University, Hangzhou, Zhejiang 310030, China.
概括
本研究引入了一种渐变意识的学习方法,以解决深度学习数据集中的类不平衡和标签噪声. 这种新的方法有效地减轻了这些偏见,改善了对复杂数据的模型性能.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 大规模数据集通常包含数据偏差,如类不平衡和标签噪声,这对深度学习模型构成重大挑战.
- 减轻这些偏差的现有方法,如重权或规范化,往往侧重于阶级不平衡,当两种偏差同时存在时可能会失败,导致噪音标签过度贴合.
研究的目的:
- 提出一种新的渐变意识的学习方法,旨在有效地应对深度学习中阶级不平衡和标签噪声的联合挑战.
- 在深度网络架构中隐含地解开标签噪音和类不平衡的影响.
主要方法:
- 使用渐变意识的学习策略,涉及选择性更新关键参数和纠正冗余参数更新.
- 这一更新规则适用于深度网络的编码器和分类器组件,以解决双重偏差.
- 该方法旨在在培训过程中隐性地解开标签噪音和阶级不平衡的影响.
主要成果:
- 实验结果表明,拟议的渐变感知学习方法在合成和现实世界数据集上都具有联合偏差的有效性.
- 该方法成功地减轻了类不平衡和标签噪声的负面影响,从而提高了模型性能.
结论:
- 拟议的渐变感知学习方法为在数据集上训练的深度学习模型提供了强大的解决方案,同时具有类不平衡和标签噪声.
- 这种方法提供了一种方法来隐性地解开这些偏见,提高模型的概括性和实际应用中的性能.
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