Compressed deep learning models are crucial for deploying computer vision systems on resource-constrained devices. However, model compression may affect robustness, especially under natural corruption. Therefore, it is important to consider robustness evaluation while validating computer vision systems. This paper presents a comprehensive evaluation of compression techniques - quantization, pruning, and weight clustering applied individually and in combination to convolutional neural networks (ResNet-50, VGG-19, and MobileNetV2). Using the CIFAR-10-C and CIFAR 100-C datasets, we analyze the trade-offs between robustness, accuracy, and compression ratio. Our results show that certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures. Utilizing multiobjective assessment, we determine the best configurations, showing that customized technique combinations produce beneficial multi-objective results. This study provides insights into selecting compression methods for robust and efficient deployment of models in corrupted real-world environments.

翻译：压缩深度学习模型对于在资源受限设备上部署计算机视觉系统至关重要。然而，模型压缩可能影响鲁棒性，尤其是在自然扰动条件下。因此，在验证计算机视觉系统时考虑鲁棒性评估十分重要。本文对量化、剪枝和权重聚类三种压缩技术进行了全面评估，这些技术被单独或组合应用于卷积神经网络（ResNet-50、VGG-19和MobileNetV2）。通过使用CIFAR-10-C和CIFAR-100-C数据集，我们分析了鲁棒性、准确率与压缩率之间的权衡关系。实验结果表明，特定压缩策略不仅能保持甚至能提升模型鲁棒性，这在架构更复杂的网络中尤为明显。通过多目标评估方法，我们确定了最佳配置方案，表明定制化的技术组合能够产生有益的多目标优化效果。本研究为在受扰动的实际环境中实现模型鲁棒高效部署的压缩方法选择提供了重要参考。