学习更多可能并不更好:视觉和语言任务中的知识可转移性
Tianwei Chen1, Noa Garcia1, Mayu Otani2
1Institute for Datability Science, Osaka University, Osaka 565-0871, Japan.
Journal of imaging
|December 27, 2024
概括
获得更多的知识并不总是有利于视觉和语言模型. 这项研究表明,知识的可转移性各不相同,并非所有添加的数据都能提高多模式任务的性能.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 机器学习的普遍趋势假定聚合各种数据集可以提高模型的整体性能.
- 然而,即使有共同的目标,跨不同任务的知识转移的有效性仍然未得到充分研究.
研究的目的:
- 研究视觉和语言模型中知识可转移性的细微差别.
- 为了确定是否增加知识的获取普遍导致在多模式任务的性能提高.
主要方法:
- 进行了广泛的交叉实验,涉及数百个试验.
- 分析了12个不同的视觉和语言任务,根据任务相关性分为四个类别.
主要成果:
- 证明知识转移并不总是有益的;一些知识会对相关任务产生负面影响.
- 观察到,同一组内的任务并不能通过知识转移一致地相互改进.
- 确定了数据集大小和预培训阶段作为影响知识转移效率的重要因素.
结论:
- 对于视觉和语言模型来说,更多的知识总是更好的假设受到挑战.
- 在多模式学习中,有效的知识转移是复杂和任务依赖的,受任务相似性以外的因素的影响.
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