Search Results for author: Jun Sun

Found 68 papers, 22 papers with code

Analyzing and controlling diversity in quantum-behaved particle swarm optimization

no code implementations9 Aug 2023 Li-Wei Li, Jun Sun, Chao Li, Wei Fang, Vasile Palade, Xiao-Jun Wu

Then, the correlations between the two types of diversities and the search performance are tested and analyzed on several benchmark functions, and the distance-to-average-point diversity is showed to have stronger association with the search performance during the evolving processes.

How Generalizable are Deepfake Detectors? An Empirical Study

1 code implementation8 Aug 2023 Boquan Li, Jun Sun, Christopher M. Poskitt

Deepfake videos and images are becoming increasingly credible, posing a significant threat given their potential to facilitate fraud or bypass access control systems.

DeepFake Detection Face Swapping

Towards Better Fairness-Utility Trade-off: A Comprehensive Measurement-Based Reinforcement Learning Framework

no code implementations21 Jul 2023 Simiao Zhang, Jitao Bai, Menghong Guan, Yihao Huang, Yueling Zhang, Jun Sun, Geguang Pu

The results demonstrate that CFU can improve the classifier on multiple fairness metrics without sacrificing its utility.


Semantic-Based Neural Network Repair

no code implementations12 Jun 2023 Richard Schumi, Jun Sun

The results show that we are able to repair 100% of a set of randomly generated neural networks (which are produced with an existing AI framework testing approach) effectively and efficiently (with an average repair time of 21. 08s) and 93. 75% of a collection of real neural network bugs (with an average time of 3min 40s).


How Sparse Can We Prune A Deep Network: A Geometric Viewpoint

1 code implementation9 Jun 2023 Qiaozhe Zhang, Ruijie Zhang, Jun Sun, Yingzhuang Liu

To address this problem, in this work we take a first principles approach, specifically, by merely enforcing the sparsity constraint on the original loss function, we're able to characterize the sharp phase transition point of pruning ratio, which corresponds to the boundary between the feasible and the infeasible, from the perspective of high-dimensional geometry.

Network Pruning

SynGraphy: Succinct Summarisation of Large Networks via Small Synthetic Representative Graphs

no code implementations15 Feb 2023 Jérôme Kunegis, Pawan Kumar, Jun Sun, Anna Samoilenko, Giuseppe Pirró

In this paper we take the problem of visualising large graphs from a novel perspective: we leave the original graph's nodes and edges behind, and instead summarise its properties such as the clustering coefficient and bipartivity by generating a completely new graph whose structural properties match that of the original graph.

Graph Mining Graph Sampling

QVIP: An ILP-based Formal Verification Approach for Quantized Neural Networks

1 code implementation10 Dec 2022 Yedi Zhang, Zhe Zhao, Fu Song, Min Zhang, Taolue Chen, Jun Sun

Experimental results on QNNs with different quantization bits confirm the effectiveness and efficiency of our approach, e. g., two orders of magnitude faster and able to solve more verification tasks in the same time limit than the state-of-the-art methods.


QEBVerif: Quantization Error Bound Verification of Neural Networks

1 code implementation6 Dec 2022 Yedi Zhang, Fu Song, Jun Sun

In this work, we propose a quantization error bound verification method, named QEBVerif, where both weights and activation tensors are quantized.


Point Cloud Quality Assessment using 3D Saliency Maps

no code implementations30 Sep 2022 Zhengyu Wang, Yujie Zhang, Qi Yang, Yiling Xu, Jun Sun, Shan Liu

Considering the importance of saliency detection in quality assessment, we propose an effective full-reference PCQA metric which makes the first attempt to utilize the saliency information to facilitate quality prediction, called point cloud quality assessment using 3D saliency maps (PQSM).

Point Cloud Quality Assessment Saliency Detection

Adaptive Fairness Improvement Based on Causality Analysis

no code implementations15 Sep 2022 Mengdi Zhang, Jun Sun

Given a discriminating neural network, the problem of fairness improvement is to systematically reduce discrimination without significantly scarifies its performance (i. e., accuracy).


TESTSGD: Interpretable Testing of Neural Networks Against Subtle Group Discrimination

no code implementations24 Aug 2022 Mengdi Zhang, Jun Sun, Jingyi Wang, Bing Sun

The experiment results show that TESTSGDis effective and efficient in identifying and measuring such subtle group discrimination that has never been revealed before.

Face Recognition Fairness +1

H2-Stereo: High-Speed, High-Resolution Stereoscopic Video System

no code implementations4 Aug 2022 Ming Cheng, Yiling Xu, Wang Shen, M. Salman Asif, Chao Ma, Jun Sun, Zhan Ma

We utilize a disparity network to transfer spatiotemporal information across views even in large disparity scenes, based on which, we propose disparity-guided flow-based warping for LSR-HFR view and complementary warping for HSR-LFR view.

Super-Resolution Vocal Bursts Intensity Prediction

Explicit Boundary Guided Semi-Push-Pull Contrastive Learning for Supervised Anomaly Detection

1 code implementation CVPR 2023 Xincheng Yao, Ruoqi Li, Jing Zhang, Jun Sun, Chongyang Zhang

In this way, our model can form a more explicit and discriminative decision boundary to distinguish known and also unseen anomalies from normal samples more effectively.

Ranked #3 on Supervised Anomaly Detection on MVTec AD (using extra training data)

Contrastive Learning Supervised Anomaly Detection

Verifying Neural Networks Against Backdoor Attacks

no code implementations14 May 2022 Long H. Pham, Jun Sun

To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is known to significantly reduce neural network performance.

Causality-based Neural Network Repair

no code implementations20 Apr 2022 Bing Sun, Jun Sun, Hong Long Pham, Jie Shi

Results also show that thanks to the causality-based fault localization, CARE's repair focuses on the misbehavior and preserves the accuracy of the neural networks.

Decision Making Fairness +1

ExAIS: Executable AI Semantics

1 code implementation20 Feb 2022 Richard Schumi, Jun Sun

In this new paradigm, AI frameworks such as TensorFlow and PyTorch play a key role, which is as essential as the compiler for traditional programs.

Logical Reasoning

Res2NetFuse: A Fusion Method for Infrared and Visible Images

no code implementations29 Dec 2021 Xu Song, Xiao-Jun Wu, Hui Li, Jun Sun, Vasile Palade

The Res2Net-based encoder is used to extract multi-scale features of source images, the paper introducing a new training strategy for training a Res2Net-based encoder that uses only a single image.

Repairing Adversarial Texts through Perturbation

no code implementations29 Dec 2021 Guoliang Dong, Jingyi Wang, Jun Sun, Sudipta Chattopadhyay, Xinyu Wang, Ting Dai, Jie Shi, Jin Song Dong

Furthermore, such attacks are impossible to eliminate, i. e., the adversarial perturbation is still possible after applying mitigation methods such as adversarial training.

Adversarial Text

No-Reference Point Cloud Quality Assessment via Domain Adaptation

1 code implementation CVPR 2022 Qi Yang, Yipeng Liu, Siheng Chen, Yiling Xu, Jun Sun

We present a novel no-reference quality assessment metric, the image transferred point cloud quality assessment (IT-PCQA), for 3D point clouds.

Domain Adaptation Point Cloud Quality Assessment

Fairness Testing of Deep Image Classification with Adequacy Metrics

no code implementations17 Nov 2021 Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang

DeepFAIT consists of several important components enabling effective fairness testing of deep image classification applications: 1) a neuron selection strategy to identify the fairness-related neurons; 2) a set of multi-granularity adequacy metrics to evaluate the model's fairness; 3) a test selection algorithm for fixing the fairness issues efficiently.

Classification Face Recognition +2

Enhancing Knowledge Tracing via Adversarial Training

1 code implementation10 Aug 2021 Xiaopeng Guo, Zhijie Huang, Jie Gao, Mingyu Shang, Maojing Shu, Jun Sun

The original and adversarial examples are further used to jointly train the KT model, forcing it is not only to be robust to the adversarial examples, but also to enhance the generalization over the original ones.

Knowledge Tracing

Probabilistic Verification of Neural Networks Against Group Fairness

no code implementations18 Jul 2021 Bing Sun, Jun Sun, Ting Dai, Lijun Zhang

Our approach has been evaluated with multiple models trained on benchmark datasets and the experiment results show that our approach is effective and efficient.


Automatic Fairness Testing of Neural Classifiers through Adversarial Sampling

no code implementations17 Jul 2021 Peixin Zhang, Jingyi Wang, Jun Sun, Xinyu Wang, Guoliang Dong, Xingen Wang, Ting Dai, Jin Song Dong

In this work, we bridge the gap by proposing a scalable and effective approach for systematically searching for discriminatory samples while extending existing fairness testing approaches to address a more challenging domain, i. e., text classification.

Fairness text-classification +1

Code Integrity Attestation for PLCs using Black Box Neural Network Predictions

no code implementations15 Jun 2021 Yuqi Chen, Christopher M. Poskitt, Jun Sun

Cyber-physical systems (CPSs) are widespread in critical domains, and significant damage can be caused if an attacker is able to modify the code of their programmable logic controllers (PLCs).

Privacy Preserving

Adversarial Attacks and Mitigation for Anomaly Detectors of Cyber-Physical Systems

no code implementations22 May 2021 Yifan Jia, Jingyi Wang, Christopher M. Poskitt, Sudipta Chattopadhyay, Jun Sun, Yuqi Chen

The threats faced by cyber-physical systems (CPSs) in critical infrastructure have motivated research into a multitude of attack detection mechanisms, including anomaly detectors based on neural network models.

Adversarial Attack

Which to Match? Selecting Consistent GT-Proposal Assignment for Pedestrian Detection

no code implementations18 Mar 2021 Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun

Consequently, we address the weakness of IoU by introducing one geometric sensitive search algorithm as a new assignment and regression metric.

Autonomous Driving Pedestrian Detection +1

Attack as Defense: Characterizing Adversarial Examples using Robustness

1 code implementation13 Mar 2021 Zhe Zhao, Guangke Chen, Jingyi Wang, Yiwei Yang, Fu Song, Jun Sun

Though various defense mechanisms have been proposed to improve robustness of deep learning software, many of them are ineffective against adaptive attacks.

MPED: Quantifying Point Cloud Distortion based on Multiscale Potential Energy Discrepancy

1 code implementation4 Mar 2021 Qi Yang, Yujie Zhang, Siheng Chen, Yiling Xu, Jun Sun, Zhan Ma

In this paper, we propose a new distortion quantification method for point clouds, the multiscale potential energy discrepancy (MPED).

Point cloud reconstruction

Revisiting Classification Perspective on Scene Text Recognition

1 code implementation22 Feb 2021 Hongxiang Cai, Jun Sun, Yichao Xiong

We demonstrate the effectiveness of the classification perspective on scene text recognition with extensive experiments.

Classification General Classification +3

RobOT: Robustness-Oriented Testing for Deep Learning Systems

1 code implementation11 Feb 2021 Jingyi Wang, Jialuo Chen, Youcheng Sun, Xingjun Ma, Dongxia Wang, Jun Sun, Peng Cheng

A key part of RobOT is a quantitative measurement on 1) the value of each test case in improving model robustness (often via retraining), and 2) the convergence quality of the model robustness improvement.

Software Engineering

Where, What, Whether: Multi-modal Learning Meets Pedestrian Detection

no code implementations CVPR 2020 Yan Luo, Chongyang Zhang, Muming Zhao, Hao Zhou, Jun Sun

i) We generate a bird view map, which is naturally free from occlusion issues, and scan all points on it to look for suitable locations for each pedestrian instance.

Pedestrian Detection

Towards Repairing Neural Networks Correctly

no code implementations3 Dec 2020 Guoliang Dong, Jun Sun, Jingyi Wang, Xinyu Wang, Ting Dai

Neural networks are increasingly applied to support decision making in safety-critical applications (like autonomous cars, unmanned aerial vehicles and face recognition based authentication).

Decision Making Face Recognition

Improving Neural Network Verification through Spurious Region Guided Refinement

1 code implementation15 Oct 2020 Pengfei Yang, Renjue Li, Jianlin Li, Cheng-Chao Huang, Jingyi Wang, Jun Sun, Bai Xue, Lijun Zhang

The core idea is to make use of the obtained constraints of the abstraction to infer new bounds for the neurons.

Holistic Combination of Structural and Textual Code Information for Context based API Recommendation

no code implementations15 Oct 2020 Chi Chen, Xin Peng, Zhenchang Xing, Jun Sun, Xin Wang, Yifan Zhao, Wenyun Zhao

APIRec-CST is a deep learning model that combines the API usage with the text information in the source code based on an API Context Graph Network and a Code Token Network that simultaneously learn structural and textual features for API recommendation.

SOCRATES: Towards a Unified Platform for Neural Network Analysis

1 code implementation22 Jul 2020 Long H. Pham, Jiaying Li, Jun Sun

Studies show that neural networks, not unlike traditional programs, are subject to bugs, e. g., adversarial samples that cause classification errors and discriminatory instances that demonstrate the lack of fairness.

Face Recognition Fairness +1

Inferring Point Cloud Quality via Graph Similarity

1 code implementation31 May 2020 Qi Yang, Zhan Ma, Yiling Xu, Zhu Li, Jun Sun

We propose the GraphSIM -- an objective metric to accurately predict the subjective quality of point cloud with superimposed geometry and color impairments.

Graph Similarity

Active Fuzzing for Testing and Securing Cyber-Physical Systems

1 code implementation28 May 2020 Yuqi Chen, Bohan Xuan, Christopher M. Poskitt, Jun Sun, Fan Zhang

Cyber-physical systems (CPSs) in critical infrastructure face a pervasive threat from attackers, motivating research into a variety of countermeasures for securing them.

Active Learning

sFuzz: An Efficient Adaptive Fuzzer for Solidity Smart Contracts

no code implementations18 Apr 2020 Tai D. Nguyen, Long H. Pham, Jun Sun, Yun Lin, Quang Tran Minh

In this work, we present an adaptive fuzzer for smart contracts on the Ethereum platform called sFuzz.

Software Engineering

Learning efficient structured dictionary for image classification

no code implementations9 Feb 2020 Zi-Qi Li, Jun Sun, Xiao-Jun Wu, He-Feng Yin

Recent years have witnessed the success of dictionary learning (DL) based approaches in the domain of pattern classification.

Classification Dictionary Learning +2

There is Limited Correlation between Coverage and Robustness for Deep Neural Networks

no code implementations14 Nov 2019 Yizhen Dong, Peixin Zhang, Jingyi Wang, Shuang Liu, Jun Sun, Jianye Hao, Xinyu Wang, Li Wang, Jin Song Dong, Dai Ting

In this work, we conduct an empirical study to evaluate the relationship between coverage, robustness and attack/defense metrics for DNN.

Face Recognition Malware Detection

Finite-Sample Analysis of Decentralized Temporal-Difference Learning with Linear Function Approximation

no code implementations3 Nov 2019 Jun Sun, Gang Wang, Georgios B. Giannakis, Qinmin Yang, Zaiyue Yang

Motivated by the emerging use of multi-agent reinforcement learning (MARL) in engineering applications such as networked robotics, swarming drones, and sensor networks, we investigate the policy evaluation problem in a fully decentralized setting, using temporal-difference (TD) learning with linear function approximation to handle large state spaces in practice.

Multi-agent Reinforcement Learning

Understanding Social Networks using Transfer Learning

no code implementations16 Oct 2019 Jun Sun, Steffen Staab, Jérôme Kunegis

A detailed understanding of users contributes to the understanding of the Web's evolution, and to the development of Web applications.

Transfer Learning

A Dual Camera System for High Spatiotemporal Resolution Video Acquisition

no code implementations28 Sep 2019 Ming Cheng, Zhan Ma, M. Salman Asif, Yiling Xu, Haojie Liu, Wenbo Bao, Jun Sun

This paper presents a dual camera system for high spatiotemporal resolution (HSTR) video acquisition, where one camera shoots a video with high spatial resolution and low frame rate (HSR-LFR) and another one captures a low spatial resolution and high frame rate (LSR-HFR) video.

Vocal Bursts Intensity Prediction

Towards Interpreting Recurrent Neural Networks through Probabilistic Abstraction

1 code implementation22 Sep 2019 Guoliang Dong, Jingyi Wang, Jun Sun, Yang Zhang, Xinyu Wang, Ting Dai, Jin Song Dong, Xingen Wang

In this work, we propose an approach to extract probabilistic automata for interpreting an important class of neural networks, i. e., recurrent neural networks.

Machine Translation Object Recognition

Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients

1 code implementation NeurIPS 2019 Jun Sun, Tianyi Chen, Georgios B. Giannakis, Zaiyue Yang

The present paper develops a novel aggregated gradient approach for distributed machine learning that adaptively compresses the gradient communication.

Learning from Adversarial Features for Few-Shot Classification

no code implementations25 Mar 2019 Wei Shen, Ziqiang Shi, Jun Sun

Then we use the adversarial region attention to aggregate the feature maps to obtain the adversarial features.

Classification Few-Shot Learning +1

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

1 code implementation6 Mar 2019 Jiangchao Yao, Ya zhang, Ivor W. Tsang, Jun Sun

We further generalize LCCN for open-set noisy labels and the semi-supervised setting.

Ranked #33 on Image Classification on Clothing1M (using extra training data)

Learning with noisy labels regression

Adversarial Sample Detection for Deep Neural Network through Model Mutation Testing

5 code implementations14 Dec 2018 Jingyi Wang, Guoliang Dong, Jun Sun, Xinyu Wang, Peixin Zhang

We thus first propose a measure of `sensitivity' and show empirically that normal samples and adversarial samples have distinguishable sensitivity.

Two-sample testing

Detecting Adversarial Samples for Deep Neural Networks through Mutation Testing

no code implementations14 May 2018 Jingyi Wang, Jun Sun, Peixin Zhang, Xinyu Wang

Recently, it has been shown that deep neural networks (DNN) are subject to attacks through adversarial samples.

Learning from Mutants: Using Code Mutation to Learn and Monitor Invariants of a Cyber-Physical System

no code implementations3 Jan 2018 Yuqi Chen, Christopher M. Poskitt, Jun Sun

Cyber-physical systems (CPS) consist of sensors, actuators, and controllers all communicating over a network; if any subset becomes compromised, an attacker could cause significant damage.

Toward `verifying' a Water Treatment System

no code implementations12 Dec 2017 Jingyi Wang, Jun Sun, Yifan Jia, Shengchao Qin, Zhiwu Xu

As the system is too complicated to be manually modeled, we apply latest automatic model learning techniques to construct a set of Markov chains through abstraction and refinement, based on two long system execution logs (one for training and the other for testing).

Deep Learning from Noisy Image Labels with Quality Embedding

no code implementations2 Nov 2017 Jiangchao Yao, Jiajie Wang, Ivor Tsang, Ya zhang, Jun Sun, Chengqi Zhang, Rui Zhang

However, the label noise among the datasets severely degenerates the \mbox{performance of deep} learning approaches.

Anomaly Detection for a Water Treatment System Using Unsupervised Machine Learning

no code implementations15 Sep 2017 Jun Inoue, Yoriyuki Yamagata, Yuqi Chen, Christopher M. Poskitt, Jun Sun

In this paper, we propose and evaluate the application of unsupervised machine learning to anomaly detection for a Cyber-Physical System (CPS).

Anomaly Detection BIG-bench Machine Learning +2

On Study of the Reliable Fully Convolutional Networks with Tree Arranged Outputs (TAO-FCN) for Handwritten String Recognition

no code implementations10 Jul 2017 Song Wang, Jun Sun, Satoshi Naoi

The handwritten string recognition is still a challengeable task, though the powerful deep learning tools were introduced.

Building Fast and Compact Convolutional Neural Networks for Offline Handwritten Chinese Character Recognition

no code implementations26 Feb 2017 Xuefeng Xiao, Lianwen Jin, Yafeng Yang, Weixin Yang, Jun Sun, Tianhai Chang

We design a nine-layer CNN for HCCR consisting of 3, 755 classes, and devise an algorithm that can reduce the networks computational cost by nine times and compress the network to 1/18 of the original size of the baseline model, with only a 0. 21% drop in accuracy.

Offline Handwritten Chinese Character Recognition

Automatically 'Verifying' Discrete-Time Complex Systems through Learning, Abstraction and Refinement

2 code implementations20 Oct 2016 Jingyi Wang, Jun Sun, Shengchao Qin, Cyrille Jegourel

The other is a probabilistic model based on which the given property is `verified'.

Software Engineering

Towards Learning and Verifying Invariants of Cyber-Physical Systems by Code Mutation

no code implementations6 Sep 2016 Yuqi Chen, Christopher M. Poskitt, Jun Sun

Cyber-physical systems (CPS), which integrate algorithmic control with physical processes, often consist of physically distributed components communicating over a network.

A CNN Based Scene Chinese Text Recognition Algorithm With Synthetic Data Engine

no code implementations7 Apr 2016 Xiaohang Ren, Kai Chen, Jun Sun

The proposed Chinese text recognition algorithm is evaluated with two Chinese text datasets.

Scene Text Recognition

A Novel Scene Text Detection Algorithm Based On Convolutional Neural Network

no code implementations7 Apr 2016 Xiaohang Ren, Kai Chen, Jun Sun

In this paper, we propose a CNN based scene text detection algorithm with a new text region extractor.

Scene Text Detection Text Detection

On Study of the Binarized Deep Neural Network for Image Classification

no code implementations24 Feb 2016 Song Wang, Dongchun Ren, Li Chen, Wei Fan, Jun Sun, Satoshi Naoi

Unlike those trials, in this paper, we focused on the basic propagation function of the artificial neural network and proposed the binarized deep neural network.

General Classification Image Classification

Random Drift Particle Swarm Optimization

no code implementations12 Jun 2013 Jun Sun, Xiao-Jun Wu, Vasile Palade, Wei Fang, Yuhui Shi

The free electron model considers that electrons have both a thermal and a drift motion in a conductor that is placed in an external electric field.

Cannot find the paper you are looking for? You can Submit a new open access paper.