  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.
