QANS: Toward Quantized Neural Network Adversarial Noise Suppression-Reference-Cited by-同舟云学术

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QANS: Toward Quantized Neural Network Adversarial Noise Suppression

Published:2023-12 Issue:12 Volume:42 Page:4858-4870
ISSN:0278-0070
Container-title:IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
language:
Short-container-title:IEEE Trans. Comput.-Aided Des. Integr. Circuits Syst.

Author:

Lin Chengmin¹^ORCID,Cheng Fei¹^ORCID,Yang Pengfei¹^ORCID,He Tianbing¹^ORCID,Liang Jinpeng¹^ORCID,Wang Quan¹^ORCID

Affiliation:

1. School of Computer Science and Technology, Xidian University, Xi’an, China

Funder

Shaanxi Key Technology Research and Development Program

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Graphics and Computer-Aided Design,Software

Link

http://xplorestaging.ieee.org/ielx7/43/10325328/10146506.pdf?arnumber=10146506

Reference52 articles.

1. Defensive quantization: When efficiency meets robustness;lin;Proc Int Conf Learn Represent,2019

2. Attack-Aware Detection and Defense to Resist Adversarial Examples

3. Adversarial machine learning at scale;kurakin;arXiv 1611 01236,2016

4. Error-silenced quantization: Bridging robustness and compactness;tang;Proc Workshop Artif Intell Safety,2020

5. Adaptive weight decay: On the fly weight decay tuning for improving robustness;ghiasi;Proc Int Conf Learn Represent,2023

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