all InfoSec news
Multi-step domain adaptation by adversarial attack to $\mathcal{H} \Delta \mathcal{H}$-divergence. (arXiv:2207.08948v1 [cs.LG])
July 20, 2022, 1:20 a.m. | Arip Asadulaev, Alexander Panfilov, Andrey Filchenkov
cs.CR updates on arXiv.org arxiv.org
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.
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
SOC 2 Manager, Audit and Certification
@ Deloitte | US and CA Multiple Locations
Information Security Engineers
@ D. E. Shaw Research | New York City
SOC Cyber Threat Intelligence Expert
@ Amexio | Luxembourg, Luxembourg, Luxembourg
Systems Engineer - SecOps
@ Fortinet | Dubai, Dubai, United Arab Emirates
Ingénieur Cybersécurité Gouvernance des projets AMR H/F
@ ASSYSTEM | Lyon, France
Senior DevSecOps Consultant
@ Computacenter | Birmingham, GB, B37 7YS