双重平衡用于多任务学习
Baijiong Lin1, Weisen Jiang2, Feiyang Ye3
1The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, 510000, China; HKUST(GZ) - SmartMore Joint Lab, Guangzhou, 510000, China.
概括
本研究介绍了双平衡多任务学习 (DB-MTL),以解决多任务学习中的性能问题,这些问题是由不平衡的任务损失和梯度造成的. DB-MTL有效地平衡任务,优于对基准数据集的现有方法.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 深度学习 (Deep Learning) 是一种深度学习.
背景情况:
- 多任务学习 (MTL) 能够同时学习多个相关任务,在各个领域表现出成功.
- 在MTL的一个关键挑战是由于任务损失和梯度尺度的差异而导致的性能妥协.
- 有效的任务平衡对于优化MTL性能至关重要.
研究的目的:
- 引入双平衡多任务学习 (DB-MTL) 以实现有效的任务平衡.
- 为了解决MTL中失衡的损失和梯度尺度引起的性能妥协.
- 在多任务学习场景中提高整体绩效.
主要方法:
- DB-MTL使用对数转换平衡任务损失.
- 梯度大小通过正常化重新缩放到使用最大梯度规范的可比大小.
- 提出的方法整合了损失尺度和梯度尺度平衡策略.
主要成果:
- 在多个基准数据集中,DB-MTL表现出一致的性能改进.
- 提出的方法有效地减轻了任务失衡的负面影响.
- 实验结果显示,DB-MTL的性能优于当前最先进的方法.
结论:
- 在多任务学习中,DB-MTL为任务平衡提供了一个强大的解决方案.
- 双平衡方法通过解决损失和梯度差异来提高模型性能.
- DB-MTL在优化多任务学习效率方面取得了重大进展.
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