通过对比补充标签来促进半监督学习
Qinyi Deng1, Yong Guo1, Zhibang Yang1
1South China University of Technology, China.
概括
本研究介绍了对比补充标记 (CCL),一种新的半监督学习 (SSL) 方法. CCL有效地利用低置信度数据,显著改善深度模型性能,特别是在标签稀缺的场景中.
科学领域:
- 机器学习 机器学习
- 人工智能的人工智能
- 计算机视觉 计算机视觉
背景情况:
- 半监督学习 (SSL) 有效地利用大型未标记的数据集进行深度模型训练.
- 伪标签是一种常见的SSL技术,但它经常丢弃低可信度预测,可能会浪费有价值的数据.
- 现有的方法很难有效地利用不确定的或低可信度的未标记数据.
研究的目的:
- 提出一种新的半监督学习方法,利用低可信度的未标记数据.
- 为了改进深度模型培训,引入对比补充标签 (CCL).
- 通过最大限度地利用所有未标记数据来提高标签稀缺环境中的性能.
主要方法:
- 开发了对比补充标签 (CCL),一种新的SSL方法.
- 通过识别和使用互补标签来形成可靠的负对,CCL利用低可信度数据.
- 采用对比学习来最大限度地利用所有未标记的数据,包括不确定的样本.
主要成果:
- 与现有的先进的SSL方法相比,CCL显著提高了性能.
- 该方法在标签稀缺的环境中表现出特别高的效率.
- 在CIFAR-10上与FixMatch相比,只用40个标记数据实现了2.43%的改进.
结论:
- 对比补充标记 (CCL) 为半监督学习提供了一种强大的新方法.
- 该方法通过补充标签和对比学习证明了利用低可信度数据的价值.
- CCL提供了实质性的性能增长,特别是当标记数据有限时.
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