InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle.Adversarial training (AT) has shown excellent high performance in defending against adversarial examples.  Based on this observation, we propose a novel and effective adversarial training method (InfoAT).  InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models.  Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.
InfoAT
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle.Adversarial training (AT) has shown excellent high performance in defending against adversarial examples. 
Adversarial training (AT)
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle.Adversarial training (AT) has shown excellent high performance in defending against adversarial examples.  Based on this observation, we propose a novel and effective adversarial training method (InfoAT).  InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models.  Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.
InfoAT
InfoAT: Improving Adversarial Training Using the Information Bottleneck Principle.Adversarial training (AT) has shown excellent high performance in defending against adversarial examples.  Based on this observation, we propose a novel and effective adversarial training method (InfoAT).  InfoAT is encouraged to find examples with high mutual information and exploit them efficiently to improve the final robustness of models.  Experimental results show that InfoAT achieves the best robustness among different datasets and models in comparison with several state-of-the-art methods.
InfoAT