80 citations · 264 across the 35 of their papers we have counts for
37 papers
A one-step generation model with a Single-Layer Transformer: Layer number re-distillation of FreeFlow
Haonan Wei, Linyuan Wang, Nuolin Sun +3
Currently, Flow matching methods aim to compress the iterative generation process of diffusion models into a few or even a single step, with MeanFlow and FreeFlow being representat…
MFI-ResNet: Efficient ResNet Architecture Optimization via MeanFlow Compression and Selective Incubation
Nuolin Sun, Linyuan Wang, Haonan Wei +2
ResNet has achieved tremendous success in computer vision through its residual connection mechanism. ResNet can be viewed as a discretized form of ordinary differential equations (…
Are classical deep neural networks weakly adversarially robust?
Nuolin Sun, Linyuan Wang, Dongyang Li +2
Adversarial attacks have received increasing attention and it has been widely recognized that classical DNNs have weak adversarial robustness. The most commonly used adversarial de…
Signal Adversarial Examples Generation for Signal Detection Network via White-Box Attack
Dongyang Li, Linyuan Wang, Guangwei Xiong +3
With the development and application of deep learning in signal detection tasks, the vulnerability of neural networks to adversarial attacks has also become a security threat to si…
Dual-Domain CLIP-Assisted Residual Optimization Perception Model for Metal Artifact Reduction
Xinrui Zhang, Ailong Cai, Shaoyu Wang +4
Metal artifacts in computed tomography (CT) imaging pose significant challenges to accurate clinical diagnosis. The presence of high-density metallic implants results in artifacts…
Aligned with LLM: a new multi-modal training paradigm for encoding fMRI activity in visual cortex
Shuxiao Ma, Linyuan Wang, Senbao Hou +1
Recently, there has been a surge in the popularity of pre trained large language models (LLMs) (such as GPT-4), sweeping across the entire Natural Language Processing (NLP) and Com…
A Multimodal Visual Encoding Model Aided by Introducing Verbal Semantic Information
Shuxiao Ma, Linyuan Wang, Bin Yan
Biological research has revealed that the verbal semantic information in the brain cortex, as an additional source, participates in nonverbal semantic tasks, such as visual encodin…
Dynamic ensemble selection based on Deep Neural Network Uncertainty Estimation for Adversarial Robustness
Ruoxi Qin, Linyuan Wang, Xuehui Du +2
The deep neural network has attained significant efficiency in image recognition. However, it has vulnerable recognition robustness under extensive data uncertainty in practical ap…
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
Shuhao Shi, Kai Qiao, Jian Chen +5
The development of social media user stance detection and bot detection methods rely heavily on large-scale and high-quality benchmarks. However, in addition to low annotation qual…
Dynamic Stochastic Ensemble with Adversarial Robust Lottery Ticket Subnetworks
Qi Peng, Wenlin Liu, Ruoxi Qin +3
Adversarial attacks are considered the intrinsic vulnerability of CNNs. Defense strategies designed for attacks have been stuck in the adversarial attack-defense arms race, reflect…
LRIP-Net: Low-Resolution Image Prior based Network for Limited-Angle CT Reconstruction
Qifeng Gao, Rui Ding, Linyuan Wang +2
In the practical applications of computed tomography imaging, the projection data may be acquired within a limited-angle range and corrupted by noises due to the limitation of scan…
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
Shuhao Shi, Jian Chen, Kai Qiao +3
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory. Previo…
Adaptive Multi-layer Contrastive Graph Neural Networks
Shuhao Shi, Pengfei Xie, Xu Luo +4
We present Adaptive Multi-layer Contrastive Graph Neural Networks (AMC-GNN), a self-supervised learning framework for Graph Neural Network, which learns feature representations of…
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…
Dynamic Defense Approach for Adversarial Robustness in Deep Neural Networks via Stochastic Ensemble Smoothed Model
Ruoxi Qin, Linyuan Wang, Xingyuan Chen +2
Deep neural networks have been shown to suffer from critical vulnerabilities under adversarial attacks. This phenomenon stimulated the creation of different attack and defense stra…
Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification
S. Shi, Kai Qiao, Shuai Yang +3
The Graph Neural Network (GNN) has been widely used for graph data representation. However, the existing researches only consider the ideal balanced dataset, and the imbalanced dat…
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…
Generative Adversarial Network-Based Sinogram Super-Resolution for Computed Tomography Imaging
Chao Tang, Wenkun Zhang, Linyuan Wang +4
Compared with the conventional 1*1 acquisition mode of projection in computed tomography (CT) image reconstruction, the 2*2 acquisition mode improves the collection efficiency of t…
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Kai Qiao, Chi Zhang, Jian Chen +3
On basis of functional magnetic resonance imaging (fMRI), researchers are devoted to designing visual encoding models to predict the neuron activity of human in response to present…
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai Qiao, Jian Chen, Linyuan Wang +3
In the visual decoding domain, visually reconstructing presented images given the corresponding human brain activity monitored by functional magnetic resonance imaging (fMRI) is di…
AdvJND: Generating Adversarial Examples with Just Noticeable Difference
Zifei Zhang, Kai Qiao, Lingyun Jiang +2
Compared with traditional machine learning models, deep neural networks perform better, especially in image classification tasks. However, they are vulnerable to adversarial exampl…
CNN-based Automatic Detection of Bone Conditions via Diagnostic CT Images for Osteoporosis Screening
Chao Tang, Wenkun Zhang, Haiting Li +6
Purpose: The purpose is to design a novelty automatic diagnostic method for osteoporosis screening by using the potential capability of convolutional neural network (CNN) in featur…
HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples
Wanting Yu, Hongyi Yu, Lingyun Jiang +2
Adversarial examples reveal the vulnerability and unexplained nature of neural networks. Studying the defense of adversarial examples is of considerable practical importance. Most…
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Kai Qiao, Chi Zhang, Jian Chen +3
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation. Visual encod…