人类分析中的合成数据:一项调查
IEEE transactions on pattern analysis and machine intelligence
|February 6, 2024
概括
合成数据生成提供了一个高效的,保护隐私的解决方案,用于在人类分析任务中训练深度神经网络. 本调查探讨了使用合成数据进行人类分析的方法,好处和挑战.
科学领域:
- 计算机视觉 计算机视觉
- 机器学习 机器学习
- 人与计算机的交互
背景情况:
- 深度神经网络 (DNN) 在人类分析任务中表现出色,例如识别和重新识别.
- DNN的性能严重依赖于大规模的训练数据集.
- 获取真实世界的数据用于人类分析是具有挑战性的,因为成本,时间和隐私问题.
研究的目的:
- 对生成和利用合成数据在人类分析中的方法进行调查.
- 突出合成数据的好处,作为现实世界数据收集的替代方案.
- 提供当前合成数据生成模型和数据集的概述.
主要方法:
- 对人类分析合成数据生成技术的文献综述.
- 分析最先进的方法及其应用.
- 编制公开可用的合成数据集和生成模型.
主要成果:
- 合成数据生成是训练DNN的可行和保护隐私的替代方案.
- 合成数据生成方法的显著进步已经被观察到.
- 许多合成数据集和模型现在可供研究人员使用.
结论:
- 合成数据有效地解决了人类分析中的数据稀缺性和隐私问题.
- 需要进一步的研究来克服现有的局限性并探索未解决的问题.
- 该调查为该领域的研究人员和从业人员提供了全面的资源.
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