当对抗性训练与快速调整相遇时:对抗性双重快速调整用于无监督域调整
概括
本研究介绍了Adversarial Dual Prompt Tuning (ADAPT),这是一种用于无监督域适应 (UDA) 的新方法,它使用文本和视觉提示来对准数据域. 在不需要明确的域区分器的情况下,ADAPT有效地调整模型,改善基准数据集的性能.
科学领域:
- 计算机科学 计算机科学
- 人工智能的人工智能
- 机器学习 机器学习
背景情况:
- 无监督域调整 (UDA) 旨在将知识从一个标记的源域转移到一个没有标记的目标域.
- 敌对训练是UDA中常见的一种技术,以尽量减少域差异.
- 快速调整提供了一种高效的方法来适应像CLIP这样的大型预训练的视觉语言模型.
研究的目的:
- 提出一种新的方法,即对抗式双快速调整 (ADAPT),用于无监督域调整.
- 为了在CLIP模型适应的协作对抗框架内利用文字和视觉提示.
- 在不依赖于明确的域区分器的情况下实现域调整.
主要方法:
- ADAPT将文本提示调整和视觉提示调整集成到一个协作对抗框架中.
- 文本提示调整旨在区分源和目标图像.
- 视觉提示调整旨在通过在全球和类别层面上建模联合概率分布,使源域和目标域对齐.
主要成果:
- 在四个基准数据集上进行了广泛的实验.
- 拟议的ADAPT方法在无监督的域调整任务中表现出显著的有效性.
- 该方法通过模拟没有明确的歧视因素的联合概率分布,成功地对准了领域.
结论:
- ADAPT提供了一种有效和新的方法,用于使用对抗性提示调来进行无监督域调整.
- 该方法通过模拟联合概率分布成功对准域,消除了对显式域区分器的需求.
- 文本和视觉提示之间的协作对抗游戏增强了CLIP的模型适应性.
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