基于伪标签的实用半监督的超级培训,用于少量学习
概括
这项研究介绍了基于伪标签的超级学习 (PLML) 对于少数射击学习 (FSL). 在半监督的元训练中,PLML有效地使用未标记的数据,以有限的标签提高FSL模型的性能.
科学领域:
- 机器学习 机器学习
- 计算机科学 计算机科学
背景情况:
- 短暂学习 (FSL) 方法通常需要大量的标记数据来进行元训练,这限制了它们的实际应用.
- 现有的FSL半监督超训练 (SSMT) 方法需要从未标记的数据中进行类意识的样本选择,这往往是不切实际的.
研究的目的:
- 为FSL提出一个实用的半监督的超级培训设置,使用真正没有标签的数据.
- 引入一种新的超级培训框架,即基于伪标签的超级学习 (PLML),以有效地利用标签和未标签的数据.
主要方法:
- 一个分类器使用半监督学习 (SSL) 进行训练,用于为未标记的数据生成伪标签.
- 短暂的任务是使用标记和伪标记数据构建的.
- 采用一种新的微调方法,包括功能平滑和噪声抑制,用于训练FSL模型对潜在的噪音伪标签进行训练.
主要成果:
- 拟议的PLML框架有效地减轻了FSL模型中的性能下降,但标记数据有限.
- 在FSL基准上,PLML显著优于现有的代表性SSMT模型.
- 超训练方法也提高了几个SSL算法的性能.
结论:
- PLML提供了一种实用且有效的解决方案,用于半监督的超级培训,用于少量学习,特别是在具有有限标记数据的场景中.
- 该框架能够利用真正没有标签的数据,使其适合于现实的应用.
- 这种方法证明了伪标签在改进FSL和SSL方法方面的潜力.
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