Model compression techniques allow to significantly reduce the computational cost associated with data processing by deep neural networks with only a minor decrease in average accuracy. Simultaneously, reducing the model size may have a large effect on noisy cases or objects belonging to less frequent classes. It is a crucial problem from the perspective of the models' safety, especially for object detection in the autonomous driving setting, which is considered in this work. It was shown in the paper that the sensitivity of compressed models to different distortion types is nuanced, and some of the corruptions are heavily impacted by the compression methods (i.e., additive noise), while others (blur effect) are only slightly affected. A common way to improve the robustness of models is to use data augmentation, which was confirmed to positively affect models' robustness, also for highly compressed models. It was further shown that while data imbalance methods brought only a slight increase in accuracy for the baseline model (without compression), the impact was more striking at higher compression rates for the structured pruning. Finally, methods for handling data imbalance brought a significant improvement of the pruned models' worst-detected class accuracy.
翻译:模型压缩技术可以大大减少与深神经网络数据处理有关的计算成本,平均精确度仅略微下降。同时,模型规模的缩小可能对噪音案例或属于较不常见类别的物体产生很大影响。从模型安全的角度来说,这是一个关键问题,特别是在自动驾驶环境中的物体探测方面,这是这项工作中考虑的问题。文件表明,压缩模型对不同扭曲类型的敏感度是细微的,有些腐败受到压缩方法(即添加噪音)的严重影响,而其他(布尔效应)则受到轻微影响。提高模型强度的一个常见办法是使用数据增强,经证实,这对模型的稳健性也有积极影响,对于高度压缩的模型也是如此。还进一步表明,虽然数据不平衡方法只使基线模型的准确性略有提高(没有压缩),但对于结构化的修剪剪的压缩率则更为明显。最后,处理数据不平衡的方法大大改进了编织模型最差的分类准确性。