Adversarial Training on Purification (AToP): Enhancing Robustness and Generalization
The author proposes Adversarial Training on Purification (AToP) as a novel defense technique to enhance both robustness and generalization in deep neural networks.
Adversarial Training on Purification (AToP): Enhancing Robustness and Generalization
AToP combines adversarial training and purification to enhance robustness and generalization against unseen attacks.
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Full adversarial examples improve pre-processing defense robustness.
Pre-processing Defenses for White-box Robustness Enhancement
Deep neural networks are vulnerable to adversarial noise, and pre-processing methods can enhance white-box robustness by utilizing full adversarial examples.
Adversarial Training on Purification (AToP): Enhancing Robustness and Generalization
AToP combines adversarial training and purification to enhance robustness and generalization.
Adversarial Training on Purification (AToP): Advancing Robustness and Generalization
Adversarial Training on Purification (AToP) combines the strengths of adversarial training and adversarial purification to enhance robustness and generalization against unseen attacks.
