Guansong Pang

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Longbing CaoDistinguished Chair Professor in AI & ARC Future Fellow (Level 3), Macquarie UniversityVerified email at mq.edu.au
Chunhua ShenZhejiang UniversityVerified email at zju.edu.cn
Anton van den HengelDirector of the Centre for Augmented Reasoning at AIML.Verified email at adelaide.edu.au
Yu TianUniversity of PennsylvaniaVerified email at pennmedicine.upenn.edu
Gustavo CarneiroProfessor of AI and Machine Learning, University of SurreyVerified email at surrey.ac.uk
Ling Chenuniversity of technology sydneyVerified email at uts.edu.au
Xiao BaiProfessor of Computer Science, Beihang UniversityVerified email at buaa.edu.cn
Cheng YanBeihang UniversityVerified email at buaa.edu.cn
Huan LiuRegents Professor, Arizona State UniversityVerified email at asu.edu
Choubo DingThe University of AdelaideVerified email at adelaide.edu.au
Hongzuo XuNational University of Defense TechnologyVerified email at nudt.edu.cn
yuyuan liuUniversity of AdelaideVerified email at student.adelaide.edu.au
Jianpeng ZhangAlibaba DAMO Academy; Zhejiang UniversityVerified email at alibaba-inc.com
Warren JinData61 CSIRO AustraliaVerified email at csiro.au
Charu AggarwalDistinguished Research Staff Member, IBM T. J. Watson Research CenterVerified email at us.ibm.com
Kai Ming TingNanjing UniversityVerified email at nju.edu.cn
Hezhe QiaoSingapore Management University (SMU)Verified email at smu.edu.sg
Peng WangSchool of Computer Science, Northwestern Polytechnical University, ChinaVerified email at nwpu.edu.cn
Peng Wu 吴鹏Northwestern Polytechnical UniversityVerified email at nwpu.edu.cn
David AlbrechtHead of Digital Technology, John Monash Science SchoolVerified email at monash.edu

Guansong Pang

Assistant Professor of Computer Science, Singapore Management University

Verified email at smu.edu.sg - Homepage

Machine Learning Data Mining Computer Vision Anomaly Detection Open-world Learning


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Deep Learning for Anomaly Detection: A Review G Pang, C Shen, L Cao, AVD Hengel ACM Computing Surveys (CSUR) 54 (2), 1-38, 2021	2223	2021
Viral Pneumonia Screening on Chest X-rays Using Confidence-Aware Anomaly Detection J Zhang, Y Xie, G Pang, Z Liao, J Verjans, W Li, Z Sun, J He, Y Li, C Shen, ... IEEE Transactions on Medical Imaging, 2021	964*	2021
An improved K-nearest-neighbor algorithm for text categorization S Jiang, G Pang, M Wu, L Kuang Expert Systems with Applications 39 (1), 1503-1509, 2012	430	2012
Deep anomaly detection with deviation networks G Pang, C Shen, A Van Den Hengel Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019	384	2019
Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning Y Tian, G Pang, Y Chen, R Singh, JW Verjans, G Carneiro Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2021	343	2021
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection G Pang, C Yan, C Shen, A van den Hengel, X Bai Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020	257	2020
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection G Pang, L Cao, L Chen, H Liu Proceedings of the 24th ACM SIGKDD International Conference on Knowledge …, 2018	237	2018
Beyond triplet loss: person re-identification with fine-grained difference-aware pairwise loss C Yan, G Pang, X Bai, C Liu, X Ning, L Gu, J Zhou IEEE Transactions on Multimedia 24, 1665-1677, 2021	210	2021
Deep isolation forest for anomaly detection H Xu, G Pang, Y Wang, Y Wang IEEE Transactions on Knowledge and Data Engineering 35 (12), 12591-12604, 2023	129	2023
Deep weakly-supervised anomaly detection G Pang, C Shen, H Jin, A van den Hengel Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and …, 2023	123	2023
Toward deep supervised anomaly detection: Reinforcement learning from partially labeled anomaly data G Pang, A van den Hengel, C Shen, L Cao Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data …, 2021	123*	2021
Deep one-class classification via interpolated gaussian descriptor Y Chen, Y Tian, G Pang, G Carneiro Proceedings of the AAAI Conference on Artificial Intelligence 36 (1), 383-392, 2022	120*	2022
Catching both gray and black swans: Open-set supervised anomaly detection C Ding, G Pang, C Shen Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022	98	2022
Constrained contrastive distribution learning for unsupervised anomaly detection and localisation in medical images Y Tian, G Pang, F Liu, Y Chen, SH Shin, JW Verjans, R Singh, G Carneiro Medical Image Computing and Computer Assisted Intervention–MICCAI 2021: 24th …, 2021	82	2021
Explainable deep few-shot anomaly detection with deviation networks G Pang, C Ding, C Shen, A Hengel arXiv preprint arXiv:2108.00462, 2021	79	2021
Self-supervised learning for time series analysis: Taxonomy, progress, and prospects K Zhang, Q Wen, C Zhang, R Cai, M Jin, Y Liu, JY Zhang, Y Liang, ... IEEE Transactions on Pattern Analysis and Machine Intelligence, 2024	78	2024
Deep graph-level anomaly detection by glocal knowledge distillation R Ma, G Pang, L Chen, A van den Hengel Proceedings of the fifteenth ACM international conference on web search and …, 2022	78	2022
Outlier detection in complex categorical data by modelling feature value couplings G Pang, L Cao, L Chen Proceedings of the 25th International Joint Conference on Artificial …, 2016	78	2016
LeSiNN: Detecting anomalies by identifying least similar nearest neighbours G Pang, KM Ting, D Albrecht 2015 IEEE international conference on data mining workshop (ICDMW), 623-630, 2015	74	2015
Pixel-wise energy-biased abstention learning for anomaly segmentation on complex urban driving scenes Y Tian, Y Liu, G Pang, F Liu, Y Chen, G Carneiro European Conference on Computer Vision, 246-263, 2022	67	2022

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