The insights gained from the study on quantized networks can be extended to other model compression techniques like pruning and knowledge distillation to enhance their adversarial robustness. Here are some ways to apply these insights to other compression techniques:
Initialization Strategies: Similar to the study's focus on the impact of initialization parameters on robustness, techniques like pruning and knowledge distillation can benefit from starting with robustly trained models as initialization. This can help improve the model's resilience to adversarial attacks during the compression process.
Training Strategies: Just as the study explored the effects of different training strategies on quantized models, techniques like pruning and knowledge distillation can leverage adversarial training to enhance their robustness. By incorporating adversarial training into the compression process, these techniques can improve their ability to withstand adversarial attacks.
Quantization Precision: The study highlighted the importance of quantization bit-width on robustness. Similarly, in pruning and knowledge distillation, optimizing the compression parameters to strike a balance between accuracy and robustness can lead to more resilient models against adversarial attacks.
Transfer Learning: Techniques like knowledge distillation often involve transfer learning from a teacher model to a student model. Insights from transfer adversarial learning in the study can be applied to improve the robustness of compressed models through knowledge distillation.
By applying the principles of robustness, initialization strategies, training techniques, and transfer learning from the study on quantized networks to other model compression techniques, it is possible to enhance the adversarial robustness of pruned or distilled models.