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Xiaochun Cao
Also published under:Xiaocun Cao, X. Cao
Affiliation
School of Cyber Science and Technology, Sun Yat-sen University, Shenzhen Campus, Shenzhen, China
Topic
Deep Neural Network,Convolutional Neural Network,Adversarial Attacks,Peak Signal-to-noise Ratio,Adversarial Examples,Attack Methods,Convolutional Layers,Training Set,Feature Maps,Adversarial Perturbations,Generative Adversarial Networks,Training Data,Attack Success Rate,Loss Function,Clear Image,Face Images,Feature Representation,Input Image,Latent Space,Neural Network,Objective Function,Real-world Datasets,Adversarial Training,Attention Mechanism,Domain Adaptation,Empirical Risk,Fast Gradient Sign Method,Feature Space,Generalization Error,High-quality Images,Image Classification,Negative Samples,Object Detection,Optical Flow,Optimization Problem,Projected Gradient Descent,Receptive Field,Representation Learning,Scoring Function,Self-supervised Learning,Spatial Domain,Training Dataset,Adversarial Robustness,Alternative Models,Black-box Attacks,Clean Samples,Data Augmentation,Decoding,Deep Learning,Deep Network,
Biography
Xiaochun Cao is currently a professor with the School of Cyber Science and Technology, Shenzhen Campus, Sun Yat-sen University, Shenzhen, China. His dissertation was nominated for the University of Central Florida's university-level Outstanding Dissertation Award. In 2004 and 2010, he was the recipient of Piero Zamperoni Best Student Paper Award at the International Conference on Pattern Recognition. He is a fellow of the IET.