一项关于源代码自由域名适应的全面调查.
IEEE transactions on pattern analysis and machine intelligence
|February 28, 2024
概括
本调查提供了源代码免费域调整 (SFDA) 的全面概述,这是一个转移学习技术,可以在不访问原始数据的情况下将模型调整为新数据. 它对方法进行了分类,并根据基准分析了它们的有效性.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 计算机视觉 计算机视觉
背景情况:
- 域调整 (DA) 对于改善不同数据分布的模型性能至关重要.
- 传统的DA需要访问源域和目标域数据,这给隐私带来了挑战.
- 无源域调整 (SFDA) 解决了这些局限性,仅使用源训练模型和未标记的目标数据.
研究的目的:
- 提供对源代码免费域调整 (SFDA) 最近进展的全面调查.
- 将SFDA方法组织成基于转移学习框架的统一分类方案.
- 分析各种SFDA方法及其组合的有效性.
主要方法:
- 系统审查和对现有的SFDA文献进行分类.
- 模块化SFDA方法组件以说明关系和机制.
- 在Office-31,Office-home和VisDA基准上对30多种SFDA方法进行实证比较.
主要成果:
- 确定SFDA方法中的关键组件和框架.
- 对比分析揭示了不同技术路线的有效性及其协同效应.
- 评估SFDA在各种分类任务中的表现.
结论:
- SFDA是一个快速发展的领域,解决了转移学习中的实际数据隐私问题.
- 该调查提供了对SFDA方法及其性能的结构化理解.
- 突出了未来的研究方向和SFDA的潜在设置.
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