@article{pang2021deep,title={Deep learning for anomaly detection: A review},author={Pang, Guansong and Shen, Chunhua and Cao, Longbing and Hengel, Anton Van Den},journal={ACM Computing Surveys (CSUR)},volume={54},number={2},pages={1--38},year={2021},publisher={ACM New York, NY, USA}}

Numerical Datasets

@inproceedings{pang2019deep,title={Deep anomaly detection with deviation networks},author={Pang, Guansong and Shen, Chunhua and van den Hengel, Anton},booktitle={Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery \& data mining},pages={353--362},year={2019}}

Categorical Datasets

@inproceedings{pang2016outlier,title={Outlier detection in complex categorical data by modelling the feature value couplings},author={Pang, Guansong and Cao, Longbing and Chen, Ling},booktitle={IJCAI International Joint Conference on Artificial Intelligence},year={2016}}@inproceedings{xu2018exploring,title={Exploring a high-quality outlying feature value set for noise-resilient outlier detection in categorical data},author={Xu, Hongzuo and Wang, Yongjun and Cheng, Li and Wang, Yijie and Ma, Xingkong},booktitle={Proceedings of the 27th ACM International Conference on Information and Knowledge Management},pages={17--26},year={2018}}@article{pang2021homophily,title={Homophily outlier detection in non-IID categorical data},author={Pang, Guansong and Cao, Longbing and Chen, Ling},journal={Data Mining and Knowledge Discovery},pages={1--62},year={2021},publisher={Springer}}

Video Datasets

@inproceedings{sultani2018real,title={Real-world anomaly detection in surveillance videos},author={Sultani, Waqas and Chen, Chen and Shah, Mubarak},booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},pages={6479--6488},year={2018}}@inproceedings{tian2021weakly,title={Weakly-supervised Video Anomaly Detection with Robust Temporal Feature Magnitude Learning},author={Tian, Yu and Pang, Guansong and Chen, Yuanhong and Singh, Rajvinder and Verjans, Johan W and Carneiro, Gustavo},booktitle={Proceedings of the IEEE/CVF international conference on computer vision},year={2021}}

Image Datasets

@article{pang2021explainable,title={Explainable Deep Few-shot Anomaly Detection with Deviation Networks},author={Pang, Guansong and Ding, Choubo and Shen, Chunhua and Hengel, Anton van den},journal={arXiv preprint arXiv:2108.00462},year={2021}}@inproceedings{ding2022catching,title={Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection},author={Ding, Choubo and Pang, Guansong and Shen, Chunhua},booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},pages={7388--7398},year={2022}}@article{tian2021pixel,title={Pixel-wise Energy-biased Abstention Learning for Anomaly Segmentation on Complex Urban Driving Scenes},author={Tian, Yu and Liu, Yuyuan and Pang, Guansong and Liu, Fengbei and Chen, Yuanhong and Carneiro, Gustavo},journal={arXiv preprint arXiv:2111.12264},year={2021}}@article{chan2021segmentmeifyoucan,title={Segmentmeifyoucan: A benchmark for anomaly segmentation},author={Chan, Robin and Lis, Krzysztof and Uhlemeyer, Svenja and Blum, Hermann and Honari, Sina and Siegwart, Roland and Salzmann, Mathieu and Fua, Pascal and Rottmann, Matthias},journal={arXiv preprint arXiv:2104.14812},year={2021}}

Graph Datasets

@inproceedings{ma2022deep,title={Deep Graph-level Anomaly Detection by Glocal Knowledge Distillation},author={Ma, Rongrong and Pang, Guansong and Chen, Ling and van den Hengel, Anton},booktitle={The Fifteenth ACM International Conference on Web Search and Data Mining},year={2022}}

@article{ding2021cross,title={Cross-domain graph anomaly detection},author={Ding, Kaize and Shu, Kai and Shan, Xuan and Li, Jundong and Liu, Huan},journal={IEEE Transactions on Neural Networks and Learning Systems},volume={33},number={6},pages={2406--2415},year={2021},publisher={IEEE}}@inproceedings{wang2023cross,title={Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive Alignment},author={Wang, Qizhou and Pang, Guansong and Salehi, Mahsa and Buntine, Wray and Leckie, Christopher},booktitle={AAAI},year={2023}}

Time Series Datasets

@article{xu2022deep,title={Calibrated One-class Classification for Unsupervised Time Series Anomaly Detection},author={Xu, Hongzuo and Wang, Yijie and Jian, Songlei and Liao, Qing and Wang, Yongjun and Pang, Guansong},journal={arXiv preprint arXiv:2207.12201},year={2022}}

@article{wu2021current,title={Current time series anomaly detection benchmarks are flawed and are creating the illusion of progress},author={Wu, Renjie and Keogh, Eamonn},journal={IEEE Transactions on Knowledge and Data Engineering},year={2021},publisher={IEEE}}