Adversarial examples are transferable between different models. In our paper, we propose to use this property for multi-step domain adaptation. In unsupervised domain adaptation settings, we demonstrate that replacing the source domain with adversarial examples to $\mathcal{H} \Delta \mathcal{H}$-divergence can improve source classifier accuracy on the target domain. Our method can be connected to most domain adaptation techniques. We conducted a range of experiments and achieved improvement in accuracy on Digits and Office-Home datasets.

翻译：不同模型之间可互换对应示例。 在我们的文件中, 我们提议使用此属性进行多步骤域适应。 在未受监督的域适应设置中, 我们证明用对称示例替换源域可以提高目标域源分类的准确性。 我们的方法可以连接到大多数域适应技术。 我们进行了一系列实验, 提高了Digits 和 Office-Home 数据集的准确性。