Search Results for author:
Found 4 papers, 0 papers with code
Towards customizable reinforcement learning agents: Enabling preference specification through online vocabulary expansion
If the relevant concept is not in the shared vocabulary, then it is learned.
PAT: Pseudo-Adversarial Training For Detecting Adversarial Videos
Extensive research has demonstrated that deep neural networks (DNNs) are prone to adversarial attacks.
Evaluating a Simple Retraining Strategy as a Defense Against Adversarial Attacks
Though deep neural networks (DNNs) have shown superiority over other techniques in major fields like computer vision, natural language processing, robotics, recently, it has been proven that they are vulnerable to adversarial attacks.
AdvFoolGen: Creating Persistent Troubles for Deep Classifiers
Researches have shown that deep neural networks are vulnerable to malicious attacks, where adversarial images are created to trick a network into misclassification even if the images may give rise to totally different labels by human eyes.
