8 citations · 27 across the 10 of their papers we have counts for
11 papers
Synergistic Network Learning and Label Correction for Noise-robust Image Classification
Chen Gong, Kong Bin, Eric J. Seibel +3
Large training datasets almost always contain examples with inaccurate or incorrect labels. Deep Neural Networks (DNNs) tend to overfit training label noise, resulting in poorer mo…
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Ziwei Luo, Jing Hu, Xin Wang +5
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces. In t…
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
Ziwei Luo, Jing Hu, Xin Wang +6
Large deformations of organs, caused by diverse shapes and nonlinear shape changes, pose a significant challenge for medical image registration. Traditional registration methods ne…
Adaptive Warden Strategy for Countering Network Covert Storage Channels
Mehdi Chourib, Steffen Wendzel, Wojciech Mazurczyk
The detection and elimination of covert channels are performed by a network node, known as a warden. Especially if faced with adaptive covert communication parties, a regular warde…
Transferable Adversarial Examples for Anchor Free Object Detection
Quanyu Liao, Xin Wang, Bin Kong +5
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result. The vulnerability has led to a sur…
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
Quanyu Liao, Xin Wang, Bin Kong +4
The deep neural network is vulnerable to adversarial examples. Adding imperceptible adversarial perturbations to images is enough to make them fail. Most existing research focuses…