Search Results for author: Kai Qiao

Found 21 papers, 4 papers with code

RF-GNN: Random Forest Boosted Graph Neural Network for Social Bot Detection

1 code implementation14 Apr 2023 Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song, Jian Chen, Bin Yan

This paper proposes a Random Forest boosted Graph Neural Network for social bot detection, called RF-GNN, which employs graph neural networks (GNNs) as the base classifiers to construct a random forest, effectively combining the advantages of ensemble learning and GNNs to improve the accuracy and robustness of the model.

Ensemble Learning feature selection +1

Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection

1 code implementation14 Feb 2023 Shuhao Shi, Kai Qiao, Jie Yang, Baojie Song, Jian Chen, Bin Yan

The proposed framework is evaluated using three real-world bot detection benchmark datasets, and it consistently exhibits superiority over the baselines.

MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark

1 code implementation3 Jan 2023 Shuhao Shi, Kai Qiao, Jian Chen, Shuai Yang, Jie Yang, Baojie Song, Linyuan Wang, Bin Yan

However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.

Node Classification Stance Detection +1

Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks

no code implementations8 May 2022 Shuhao Shi, Jian Chen, Kai Qiao, Shuai Yang, Linyuan Wang, Bin Yan

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.

Node Classification

Adaptive Multi-layer Contrastive Graph Neural Networks

no code implementations29 Sep 2021 Shuhao Shi, Pengfei Xie, Xu Luo, Kai Qiao, Linyuan Wang, Jian Chen, Bin Yan

AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks.

Data Augmentation Self-Supervised Learning

ShapeEditer: a StyleGAN Encoder for Face Swapping

no code implementations26 Jun 2021 Shuai Yang, Kai Qiao

In this paper, we propose a novel encoder, called ShapeEditor, for high-resolution, realistic and high-fidelity face exchange.

Attribute Face Swapping

Improving the Transferability of Adversarial Examples with New Iteration Framework and Input Dropout

no code implementations3 Jun 2021 Pengfei Xie, Linyuan Wang, Ruoxi Qin, Kai Qiao, Shuhao Shi, Guoen Hu, Bin Yan

In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three.

Boosting-GNN: Boosting Algorithm for Graph Networks on Imbalanced Node Classification

no code implementations25 May 2021 S. Shi, Kai Qiao, Shuai Yang, L. Wang, J. Chen, Bin Yan

Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN.

Ensemble Learning Node Classification +1

Defense-guided Transferable Adversarial Attacks

no code implementations22 Oct 2020 Zifei Zhang, Kai Qiao, Jian Chen, Ningning Liang

Experimentally, we show that our ASR of adversarial attack reaches to 58. 38% on average, which outperforms the state-of-the-art method by 12. 1% on the normally trained models and by 11. 13% on the adversarially trained models.

Adversarial Attack

Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features

no code implementations26 Mar 2020 Kai Qiao, Chi Zhang, Jian Chen, Linyuan Wang, Li Tong, Bin Yan

Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies.

General Classification Image Classification

BigGAN-based Bayesian reconstruction of natural images from human brain activity

no code implementations13 Mar 2020 Kai Qiao, Jian Chen, Linyuan Wang, Chi Zhang, Li Tong, Bin Yan

In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.

Conditional Image Generation Generative Adversarial Network

AdvJND: Generating Adversarial Examples with Just Noticeable Difference

no code implementations1 Feb 2020 Zifei Zhang, Kai Qiao, Lingyun Jiang, Linyuan Wang, Bin Yan

To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference coefficients, in the constraint of a distortion function when generating adversarial examples.

Image Classification

HAD-GAN: A Human-perception Auxiliary Defense GAN to Defend Adversarial Examples

no code implementations17 Sep 2019 Wanting Yu, Hongyi Yu, Lingyun Jiang, Mengli Zhang, Kai Qiao

The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN).

Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization

1 code implementation27 Jul 2019 Kai Qiao, Chi Zhang, Jian Chen, Linyuan Wang, Li Tong, Bin Yan

Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.


Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense

no code implementations12 Apr 2019 Lingyun Jiang, Kai Qiao, Ruoxi Qin, Linyuan Wang, Jian Chen, Haibing Bu, Bin Yan

In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.

Adversarial Attack Image Classification

Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices

no code implementations19 Mar 2019 Kai Qiao, Jian Chen, Linyuan Wang, Chi Zhang, Lei Zeng, Li Tong, Bin Yan

Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively.

Neurons and Cognition

A visual encoding model based on deep neural networks and transfer learning

no code implementations23 Feb 2019 Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Guoen Hu, Ruyuan Zhang, Bin Yan

In this framework, we employ the transfer learning technique to incorporate a pre-trained DNN (i. e., AlexNet) and train a nonlinear mapping from visual features to brain activity.

Transfer Learning

Constraint-free Natural Image Reconstruction from fMRI Signals Based on Convolutional Neural Network

no code implementations16 Jan 2018 Chi Zhang, Kai Qiao, Linyuan Wang, Li Tong, Ying Zeng, Bin Yan

Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN).

Image Reconstruction

Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture

no code implementations2 Jan 2018 Kai Qiao, Chi Zhang, Linyuan Wang, Bin Yan, Jian Chen, Lei Zeng, Li Tong

We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner.

Open-Ended Question Answering SSIM

Image Prediction for Limited-angle Tomography via Deep Learning with Convolutional Neural Network

no code implementations29 Jul 2016 Hanming Zhang, Liang Li, Kai Qiao, Linyuan Wang, Bin Yan, Lei LI, Guoen Hu

The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography.

Computed Tomography (CT)

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