| Deep Learning for Anomaly Detection: A Review G Pang, C Shen, L Cao, AVD Hengel ACM Computing Surveys (CSUR) 54 (2), 1-38, 2021 | 1928 | 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, 2020 | 907* | 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 | 410 | 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 | 352 | 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 | 279 | 2021 |
| 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 | 228 | 2018 |
| 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 | 226 | 2020 |
| 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 | 198 | 2021 |
| 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 | 116* | 2021 |
| 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 | 114* | 2023 |
| 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 | 109* | 2022 |
| Deep isolation forest for anomaly detection H Xu, G Pang, Y Wang, Y Wang IEEE Transactions on Knowledge and Data Engineering, 2023 | 84 | 2023 |
| 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 | 79 | 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 | 75 | 2021 |
| Explainable deep few-shot anomaly detection with deviation networks G Pang, C Ding, C Shen, A Hengel arXiv preprint arXiv:2108.00462, 2021 | 74 | 2021 |
| 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 | 74 | 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 | 68 | 2015 |
| 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 | 62 | 2022 |
| 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 | 61 | 2018 |
| 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 | 54 | 2022 |
Chunhua ShenZhejiang University在 zju.edu.cn 的电子邮件经过验证
