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深度个人主动学习:保护神经网络免受分布外挑战
Shachar Shayovitz1, Koby Bibas1, Meir Feder1
1School of Electrical Engineering, Tel Aviv University, Tel Aviv 6997801, Israel.
Entropy (Basel, Switzerland)
|February 23, 2024
概括
本研究介绍了一种高效的主动学习 (AL) 算法,可以最大限度地减少数据注释需求,特别是在分布之外的数据中. 这种新的方法显著减少了跨多个数据集的训练集大小.
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:
- 积极学习 (AL) 旨在通过战略性选择培训样本来降低数据注释成本.
- 传统的AL方法通常假定数据分布的一致性,这并不总是可行的,特别是在隐私敏感的场景中.
研究的目的:
- 为个别设置开发一个高效的主动学习算法,专注于尽量减少最小-最大遗憾.
- 解决神经网络主动学习标准的计算复杂性.
主要方法:
- 该研究提出了一个主动学习标准,该标准是基于最小化最小-最大遗憾在一个小的未标记的测试组样本.
- 开发了一种高效的算法,以接近神经网络的这个标准,解决计算挑战.
主要成果:
- 拟议的算法大大减少了所需的训练集大小,在CIFAR10上减少了高达15.4%,在EMNIST上减少了11%,在MNIST上减少了35.1%.
- 这种有效性在处理非分销数据时尤为明显.
结论:
- 开发的积极学习算法为数据选择提供了有效的解决方案,特别是在分布转移的场景中.
- 这种方法提高了模型性能,同时最大限度地减少了对大量数据注释的需求,对于对隐私敏感的应用程序来说非常有价值.
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