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

• URL: http://arxiv.org/abs/2207.08948v1
• Date: Mon, 18 Jul 2022 21:24:05 GMT
• Title: Multi-step domain adaptation by adversarial attack to $\mathcal{H} \Delta \mathcal{H}$-divergence
• Authors: Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov
• Abstract summary: In unsupervised domain adaptation settings, we demonstrate that replacing the source domain with adversarial examples to improve source accuracy on the target domain. We conducted a range of experiments and achieved improvement in accuracy on Digits and Office-Home datasets.
• Score: 73.89838982331453
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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