Следене
Guansong Pang
Guansong Pang
Assistant Professor of Computer Science, Singapore Management University
Потвърден имейл адрес: smu.edu.sg - Начална страница
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Позовавания
Позовавания
Година
Deep Learning for Anomaly Detection: A Review
G Pang, C Shen, L Cao, AVD Hengel
ACM Computing Surveys (CSUR) 54 (2), 1-38, 2021
17592021
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, 2020
884*2020
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
4012012
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
3262019
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
2462021
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
2162020
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
2132018
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
1912021
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
105*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
99*2022
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
98*2023
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
742016
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
702021
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
672022
Explainable deep few-shot anomaly detection with deviation networks
G Pang, C Ding, C Shen, A Hengel
arXiv preprint arXiv:2108.00462, 2021
672021
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
642015
Deep isolation forest for anomaly detection
H Xu, G Pang, Y Wang, Y Wang
IEEE Transactions on Knowledge and Data Engineering, 2023
632023
Sparse Modeling-based Sequential Ensemble Learning for Effective Outlier Detection in High-dimensional Numeric Data
G Pang, L Cao, L Chen, D Lian, H Liu
Thirty-Second AAAI Conference on Artificial Intelligence, 2018
602018
Unsupervised Feature Selection for Outlier Detection by Modelling Hierarchical Value-Feature Couplings
G Pang, L Cao, L Chen, H Liu
2016 IEEE 16th International Conference on Data Mining (ICDM-16), 2016
532016
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
522022
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