标签空间诱导的伪标签精细化用于多源黑盒域名适应.
概括
本研究介绍了多源黑子域调整 (MSBDA) 的标签空间诱导伪标签改进 (LPR). 通过使用源API预测,LPR改进了伪标签,提高了无需访问源数据的目标模型适应性.
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:
- 无监督域名适应 (UDA) 通常需要源数据/模型,这引发了隐私和IP方面的担忧.
- 黑盒域调整 (BDA) 使用API预测进行伪标签,但在多源设置方面存在困难.
- 现有的多源BDA方法缺乏有效的伪标签生成策略.
研究的目的:
- 为多源黑子域调整 (MSBDA) 开发一个新的培训框架.
- 通过学习多个源域之间的关系来改进伪标签的方法.
- 为了使有效的目标模型适应只使用源API预测.
主要方法:
- 为MSBDA提出的标签空间诱导伪标签精制 (LPR) 框架.
- 引入了一个伪标签炼油网络 (PRN) 来学习源域之间的关系.
- 采用双相PRN:用于初始伪标签的预热阶段和改进阶段以提高准确性.
主要成果:
- 通过利用源域之间的关系,LPR有效地改进了伪标签.
- 双相PRN成功地适应了目标模型,减轻了噪音样本.
- 在各种领域适应设置中,在四个基准数据集上实现了竞争性表现.
结论:
- 对于MSBDA,LPR提供了一个强大的解决方案,克服了现有的BDA方法的局限性.
- 该框架通过学习的域关系来证明伪标签改进的有效性.
- 为拟议的机制提供理论支持,验证其有效性.
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