BriefGPT.xyz
Dec, 2020
压缩模型通用攻击的鲁棒性和可转移性
Robustness and Transferability of Universal Attacks on Compressed Models
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Alberto G. Matachana, Kenneth T. Co, Luis Muñoz-González, David Martinez, Emil C. Lupu
TL;DR
本文研究神经网络压缩、优化技术如剪枝、量化对抗攻击的影响,并比较分析了压缩模型与未压缩模型的对抗攻击鲁棒性,发现不同压缩方法存在差异,并且不同应用具有不同表现。
Abstract
neural network compression
methods like
pruning
and
quantization
are very effective at efficiently deploying Deep Neural Networks (DNNs) o
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