论文标题
在没有源数据的情况下，无监督的域适应
Unsupervised Domain Adaptation in the Absence of Source Data
论文作者
论文摘要
当前的无监督域适应方法可以解决多种类型的分布变化，但是他们假设来自源域中的数据是可以自由使用的。随着预训练模型的使用变得更加普遍，可以合理地假设源数据不可用。我们提出了一种无监督的方法，将源分类器适应沿天然轴的源域而变化的目标域，例如亮度和对比度。我们的方法仅需要访问未标记的目标实例和源分类器。我们在分布变化涉及亮度，对比度和旋转的情况下验证我们的方法，并表明它在标记有限的数据的情况下优于微调基线。
Current unsupervised domain adaptation methods can address many types of distribution shift, but they assume data from the source domain is freely available. As the use of pre-trained models becomes more prevalent, it is reasonable to assume that source data is unavailable. We propose an unsupervised method for adapting a source classifier to a target domain that varies from the source domain along natural axes, such as brightness and contrast. Our method only requires access to unlabeled target instances and the source classifier. We validate our method in scenarios where the distribution shift involves brightness, contrast, and rotation and show that it outperforms fine-tuning baselines in scenarios with limited labeled data.
