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RobustMQ: benchmarking robustness of quantized models by Yisong Xiao; Aishan Liu; Tianyuan Zhang; Haotong Qin; Jinyang Guo; Xianglong Liu is a Computer Science article available to read on EtoBox.

What is RobustMQ: benchmarking robustness of quantized models about?

## Abstract Quantization has emerged as an essential technique for deploying deep neural networks (DNNs) on devices with limited resources. However, quantized models exhibit vulnerabilities when exposed to various types of noise in real-world applications. Despite the importance of evaluating the impact of quantization on robustness, existing research on this topic is limited and often disregards established principles of robustness evaluation, resulting in incomplete and inconclusive findings. To address this gap, we thoroughly evaluated the robustness of quantized models against various types of noise (adversarial attacks, natural corruption, and systematic noise) on ImageNet. The comprehensive evaluation results empirically provide valuable insights into the robustness of quantized models in various scenarios. For example: 1) quantized models exhibit higher adversarial robustness than their floating-point counterparts, but are more vulnerable to natural corruption and systematic noise; 2) in general, increasing the quantization bit-width results in a decrease in adversarial robustness, an increase in natural robustness, and an increase in systematic robustness; 3) among corrupti

Who reads RobustMQ: benchmarking robustness of quantized models?

Author

Yisong Xiao; Aishan Liu; Tianyuan Zhang; Haotong Qin; Jinyang Guo; Xianglong Liu

Publisher

Springer Science and Business Media LLC

Published

2023

Field

Computer Science (Physical Sciences)