RobustMQ: Benchmarking Robustness of Quantized Models. (arXiv:2308.02350v1 [cs.LG])

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 noises 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 noises (adversarial attacks, …

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