model
MNIST
CIAR10
attack
LQ
HQ
Poison
Adv
(1) Improving the overall accuracy of a model, (2) Fixing security vulnerabilities caused by poisoning of training data and (3) Improving the robustness of the network against adversarial attacks. Our evaluation on MNIST and CIFAR-10 models shows that NNrepair can improve the accuracy by 45.56 percentage points on poisoned data and 10.40 percentage points on adversarial data.
I'm an graduate student majoring in Computer Engineering at Inha University. I'm interested in Machine learning developing frameworks, Formal verification, and Concurrency.