SysNoise: Exploring and Benchmarking Training-Deployment System
  Inconsistency
Yan Wang Yuhang Li Ruihao Gong Aishan Liu Yanfei Wang Jian Hu Yongqiang Yao Yunchen Zhang Tianzi Xiao Fengwei Yu Xianglong Liu
Extensive studies have shown that deep learning models are vulnerable to
adversarial and natural noises, yet little is known about model robustness on
noises caused by different system implementations. In this paper, we for the
first time introduce SysNoise, a frequently occurred but often overlooked noise
in the deep learning training-deployment cycle. In particular, SysNoise happens
when the source training system switches to a disparate target system in
deployments, where various tiny system mismatch adds up to a non-negligible
difference. We first identify and classify SysNoise into three categories based
on the inference stage; we then build a holistic benchmark to quantitatively
measure the impact of SysNoise on 20+ models, comprehending image
classification, object detection, instance segmentation and natural language
processing tasks. Our extensive experiments revealed that SysNoise could bring
certain impacts on model robustness across different tasks and common
mitigations like data augmentation and adversarial training show limited
effects on it. Together, our findings open a new research topic and we hope
this work will raise research attention to deep learning deployment systems
accounting for model performance. We have open-sourced the benchmark and
framework at https://modeltc.github.io/systemnoise_web.