社区规范的自我训练,以学习少有标签.
Proceedings of the ... AAAI Conference on Artificial Intelligence. AAAI Conference on Artificial Intelligence
|February 9, 2024
概括
这项研究引入了一种基于社区的新方法,通过减少杂的伪标签来改善深度神经网络 (DNN) 的自我训练. 该方法提高了半监督学习任务的性能和效率.
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:
- 深度神经网络 (DNN) 训练通常需要大量的标记数据,这构成了重大的注释挑战.
- 自我训练是一种半监督的学习技术,利用未标记的数据,但由于噪音严重的伪标签,容易导致性能下降.
- 解决标签噪声对于有效和高效的模型培训与有限的监督至关重要.
研究的目的:
- 开发一种强大的自我训练方法,减轻噪音伪标签的影响.
- 在半监督学习场景中提高深度神经网络的性能和可靠性.
- 通过优化样本选择,提高培训过程的效率.
主要方法:
- 提出了一个基于社区的样本选择策略,以识别和过噪音伪标签.
- 通过在多个训练轮中汇总预测来稳定自我训练过程.
- 该方法侧重于这样一个原则:类似的标签往往与类似的数据表示相对应.
主要成果:
- 与现有的自我训练基线相比,拟议的方法显示出更高的性能,在文本数据集上获得1.83%,在图形数据集上获得2.51%的平均性能增长.
- 数据选择策略有效地减少了伪标签噪声的36.8%.
- 与最好的基线方法相比,观察到57.3%的显著时间节省.
结论:
- 基于社区的样本选择方法为自我训练中的杂伪标签提供了一个有希望的解决方案.
- 该方法在有限的监督下提高了深度神经网络训练的准确性和效率.
- 这项工作有助于推进实践应用的半监督学习技术.
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