Convolutional Neural Networks (CNNs) have achieved state-of-the-art
performance in many computer vision tasks. However, high computational and
storage demands hinder their deployment into resource-constrained environments,
such as embedded devices. Model pruning helps to meet these restrictions by
reducing the model size, while maintaining superior performance. Meanwhile,
safety-critical applications pose more than just resource and performance
constraints. In particular, predictions must not be overly confident, i.e.,
provide properly calibrated uncertainty estimations (proper uncertainty
calibration), and CNNs must be robust against corruptions like naturally
occurring input perturbations (natural corruption robustness). This work
investigates the important trade-off between uncertainty calibration, natural
corruption robustness, and performance for current state-of-research post-hoc
CNN pruning techniques in the context of image classification tasks. Our study
reveals that post-hoc pruning substantially improves the model's uncertainty
calibration, performance, and natural corruption robustness, sparking hope for
safe and robust embedded CNNs.Furthermore, uncertainty calibration and natural
corruption robustness are not mutually exclusive targets under pruning, as
evidenced by the improved safety aspects obtained by post-hoc unstructured
pruning with increasing compression.

当前研究中对图像分类任务进行的后期 CNN 剪枝技术的实证研究揭示了后期剪枝显著提高了模型的不确定性校准、性能和自然损坏鲁棒性，为安全和稳健的嵌入式 CNN 带来了希望。此外，剪枝不排斥不确定性校准和自然损坏鲁棒性，如通过后期非结构化剪枝的压缩增加的安全性方面的进一步改善所证明的。