  Increasing the model capacity is a known approach to enhance the adversarial
robustness of deep learning networks. On the other hand, various model
compression techniques, including pruning and quantization, can reduce the size
of the network while preserving its accuracy. Several recent studies have
addressed the relationship between model compression and adversarial
robustness, while some experiments have reported contradictory results. This
work summarizes available evidence and discusses possible explanations for the
observed effects.
