Web: http://arxiv.org/abs/2206.07406

June 16, 2022, 1:10 a.m. | Jonah O'Brien Weiss, Tiago Alves, Sandip Kundu

cs.LG updates on arXiv.org arxiv.org

The prevalence and success of Deep Neural Network (DNN) applications in
recent years have motivated research on DNN compression, such as pruning and
quantization. These techniques accelerate model inference, reduce power
consumption, and reduce the size and complexity of the hardware necessary to
run DNNs, all with little to no loss in accuracy. However, since DNNs are
vulnerable to adversarial inputs, it is important to consider the relationship
between compression and adversarial robustness. In this work, we investigate
the adversarial …

arxiv attacks compression lg network pruning transfer

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Researcher - Saalfeld Lab

@ Howard Hughes Medical Institute - Chevy Chase, MD | Ashburn, Virginia

Project Director, Machine Learning in US Health

@ ideas42.org | Remote, US

Data Science Intern

@ NannyML | Remote

Machine Learning Engineer NLP/Speech

@ Play.ht | Remote

Research Scientist, 3D Reconstruction

@ Yembo | Remote, US

Clinical Assistant or Associate Professor of Management Science and Systems

@ University at Buffalo | Buffalo, NY