使用带有标签噪声的样本来实现强大的持续学习
Hongyi Nie1, Shiqi Fan2, Yang Liu3
1School of Mechanical Engineering, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China; Shenzhen Research Institute, Northwestern Polytechnical University, Shenzhen, 518057, Guangdong, China.
概括
这项研究引入了一种新的方法,有效地利用标签噪声在持续的机器学习,解决标签转移的挑战. 拟议的转移适应噪声利用 (SANU) 方法通过重新注释噪声样本以提高性能来提高模型的稳定性.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机科学 计算机科学
背景情况:
- 利用带有标签噪声的样本可以提高模型的稳定性.
- 现有的方法假设一个一致的标签空间,这是由于标签转移而失败的持续学习.
- 持续学习环境中的标签转换可能会加剧噪音并降低性能.
研究的目的:
- 为了解决持续机器学习现有方法的局限性,用标签噪音.
- 提出一种新的方法,即转移适应性噪声利用 (SANU),用于将噪声样本转化为可用于持续学习的可用数据.
- 为了减轻标签转移问题,并在动态学习环境中增强模型性能.
主要方法:
- SANU采用源检测机制来识别噪音样品的正确标签空间.
- 使用元知识表示模块来改善检测过程的概括性.
- 使用标签猜测和生成策略重新注释噪音样本,以适应标签转移.
主要成果:
- SANU有效地减轻了持续学习中的标签转移问题.
- 该方法通过利用重新注释的噪音样本,显著提高了模型性能.
- 三个持续学习数据集的实验结果验证了SANU的有效性.
结论:
- SANU成功地将噪音数据转化为用于持续学习的有价值的培训输入.
- 拟议的方法为在标签转移条件下处理标签噪声提供了可靠的解决方案.
- 这项工作推动了在动态机器学习场景中利用噪音数据的发展.
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