Web: http://arxiv.org/abs/2209.07399

Sept. 16, 2022, 1:15 a.m. | Edoardo Debenedetti, Vikash Sehwag, Prateek Mittal

cs.CV updates on arXiv.org arxiv.org

In this paper, we ask whether Vision Transformers (ViTs) can serve as an
underlying architecture for improving the adversarial robustness of machine
learning models against evasion attacks. While earlier works have focused on
improving Convolutional Neural Networks, we show that also ViTs are highly
suitable for adversarial training to achieve competitive performance. We
achieve this objective using a custom adversarial training recipe, discovered
using rigorous ablation studies on a subset of the ImageNet dataset. The
canonical training recipe for ViTs …

arxiv light recipe transformers vision

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

Postdoctoral Fellow: ML for autonomous materials discovery

@ Lawrence Berkeley National Lab | Berkeley, CA

Research Scientists

@ ODU Research Foundation | Norfolk, Virginia

Embedded Systems Engineer (Robotics)

@ Neo Cybernetica | Bedford, New Hampshire

2023 Luis J. Alvarez and Admiral Grace M. Hopper Postdoc Fellowship in Computing Sciences

@ Lawrence Berkeley National Lab | San Francisco, CA

Senior Manager Data Scientist

@ NAV | Remote, US

Senior AI Research Scientist

@ Earth Species Project | Remote anywhere

Research Fellow- Center for Security and Emerging Technology (Multiple Opportunities)

@ University of California Davis | Washington, DC

Staff Fellow - Data Scientist

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Staff Fellow - Senior Data Engineer

@ U.S. FDA/Center for Devices and Radiological Health | Silver Spring, Maryland

Research Engineer - VFX, Neural Compositing

@ Flawless | Los Angeles, California, United States

[Job-TB] Senior Data Engineer

@ CI&T | Brazil

Data Analytics Engineer

@ The Fork | Paris, France