79 citations · 86 across the 5 of their papers we have counts for
6 papers
Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout
Pengfei Xie, Linyuan Wang, Ruoxi Qin +4
Deep neural networks(DNNs) is vulnerable to be attacked by adversarial examples. Black-box attack is the most threatening attack. At present, black-box attack methods mainly adopt…
Dual-energy CT Reconstruction from Dual Quarter Scans
Wenkun Zhang, Ningning Liang, Linyuan Wang +7
Compared with conventional single-energy computed tomography (CT), dual-energy CT (DECT) provides better material differentiation but most DECT imaging systems require dual full-an…
A visual encoding model based on deep neural networks and transfer learning
Chi Zhang, Kai Qiao, Linyuan Wang +4
Background: Building visual encoding models to accurately predict visual responses is a central challenge for current vision-based brain-machine interface techniques. To achieve hi…
Dissociable neural representations of adversarially perturbed images in convolutional neural networks and the human brain
Chi Zhang, Xiaohan Duan, Linyuan Wang +5
Despite the remarkable similarities between convolutional neural networks (CNN) and the human brain, CNNs still fall behind humans in many visual tasks, indicating that there still…
Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network
Hanming Zhang, Liang Li, Kai Qiao +4
Limited angle problem is a challenging issue in x-ray computed tomography (CT) field. Iterative reconstruction methods that utilize the additional prior can suppress artifacts and…
Efficient Image Reconstruction and Practical Decomposition for Dual-energy Computed Tomography
Lei Li, Ailong Cai, Linyuan Wang +6
Dual-energy computed tomography (DECT) has shown great potential and promising applications in advanced imaging fields for its capabilities of material decomposition. However, imag…