Towards Adversarial Training with Moderate Performance Improvement for Neural Network Classification

Understand

It has been demonstrated that deep neural networks are prone to noisy examples particular adversarial samples during inference process.

• The gap between robust deep learning systems in real world applications and vulnerable neural networks is still large.
• Current adversarial training strategies improve the robustness against adversarial samples.
• However, these methods lead to accuracy reduction when the input examples are clean thus hinders the practicability.