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.
Adversarial Training on Purification (AToP): Enhancing Robustness and Generalization
AToP combines adversarial training and purification to enhance robustness and generalization.
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.
Improving White-box Robustness of Pre-processing Defenses via Joint Adversarial Training
Full adversarial examples improve pre-processing defense robustness.
Adversarial Training on Purification (AToP): Enhancing Robustness and Generalization
AToP combines adversarial training and purification to enhance robustness and generalization against unseen attacks.
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.
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