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

Multi-step domain adaptation by adversarial attack to $\mathcal{H} \Delta \mathcal{H}$-divergence