Search Results for author:
Found 5 papers, 0 papers with code
A Layer-wise Adversarial-aware Quantization Optimization for Improving Robustness
After quantization, the cost can be greatly saved, and the quantized models are more hardware friendly with acceptable accuracy loss.
Two-stream Convolutional Networks for Multi-frame Face Anti-spoofing
Face anti-spoofing is an important task to protect the security of face recognition.
Improving Adversarial Robustness in Weight-quantized Neural Networks
Neural networks are getting deeper and more computation-intensive nowadays.
Feedback Learning for Improving the Robustness of Neural Networks
Recent research studies revealed that neural networks are vulnerable to adversarial attacks.
MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks
Our experiments show that different adversarial strengths, i. e., perturbation levels of adversarial examples, have different working zones to resist the attack.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
