Multi-step domain adaptation by adversarial attack to $\mathcal{H} Δ\mathcal{H}$-divergence
This addresses domain adaptation for machine learning models, but it is incremental as it builds on existing techniques.
The paper tackles the problem of unsupervised domain adaptation by using adversarial examples to improve source classifier accuracy on the target domain, achieving improvements in accuracy on Digits and Office-Home datasets.
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 $\mathcal{H} Δ\mathcal{H}$-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.