Toward Explainable Deep Anomaly Detection
Open accessAbstract
Anomaly explanation, also known as anomaly localization, is as important as, if not more than, anomaly detection in many real-world applications. However, it is challenging to build explainable detection models due to the lack of anomaly-supervisory information and the unbounded nature of anomaly; most existing studies exclusively focus on the detection task only, including the recently emerging deep learning-based anomaly detection that leverages neural networks to learn expressive low-dimensional representations or anomaly scores for the detection task. Deep learning models, including deep anomaly detection models, are often constructed as black boxes, which have been criticized for the lack of explainability of their prediction results. To tackle this explainability issue, there have been numerous techniques introduced over the years, many of which can be utilized or adapted to offer highly explainable detection results.
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Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837 · see citing works
Self-citationReal-World Anomaly Detection in Surveillance Videos
Sultani, W., Chen, C., & Shah, M. A. (2018) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2018.00678 · Cited by 2,248 · see citing works
Definitions, methods, and applications in interpretable machine learning
Murdoch, W. J., Singh, C., Kumbier, K., Abbasi-Asl, R., & Yu, B. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900654116 · Cited by 2,167 · cited 1× · see citing works
“techniques introduced over the years [10, 19].”
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., et al. (2020) · Singapore Management University Institutional Knowledge (InK) (Singapore Management University) · doi:10.48550/arxiv.2007.02500 · Cited by 1,718 · see citing works
Self-citationAnomaly Detection with Robust Deep Autoencoders
Zhou, C., & Paffenroth, R. C. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098052 · Cited by 1,548 · see citing works
Techniques for interpretable machine learning
Du, M., Liu, N., & Hu, X. (2019) · Communications of the ACM · doi:10.1145/3359786 · Cited by 1,268 · see citing works
Adversarially Learned One-Class Classifier for Novelty Detection
Sabokrou, M., Khalooei, M., Fathy, M., & Adeli, E. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00356 · Cited by 765 · see citing works
Feature bagging for outlier detection
Lazarević, A. M., & Kumar, V. (2005) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/1081870.1081891 · Cited by 672 · see citing works
Outlier Detection with Autoencoder Ensembles
Chen, J., Sathe, S. K., Aggarwal, C. C., & Turaga, D. S. (2017) · Society for Industrial and Applied Mathematics eBooks · doi:10.1137/1.9781611974973.11 · Cited by 472 · see citing works
Self-citationDeep Anomaly Detection with Deviation Networks
Pang, G., Shen, C., & van den Hengel, A. (2019) · ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD) · doi:10.1145/3292500.3330871 · Cited by 455 · see citing works
Self-citationUnmasking the Abnormal Events in Video
Ionescu, R. T., Smeureanu, S., Alexe, B., & Popescu, M. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.315 · Cited by 349 · see citing works
Self-Trained Deep Ordinal Regression for End-to-End Video Anomaly Detection
Pang, G., Cheng, Y., Shen, C., van den Hengel, A., & Bai, X. P. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.01219 · Cited by 272 · see citing works
Self-citationLearning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
Pang, G., Cao, L., Chen, L., & Liu, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220042 · Cited by 234 · see citing works
Self-citationOne-Class Adversarial Nets for Fraud Detection
Zheng, P., Yuan, S., Wu, X., Li, J., & Lu, A. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33011286 · Cited by 173 · see citing works
Detecting outlying properties of exceptional objects
Angiulli, F., Fassetti, F., & Palopoli, L. (2009) · ACM Transactions on Database Systems · doi:10.1145/1508857.1508864 · Cited by 88 · see citing works
Discovering outlying aspects in large datasets
Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J. (2016) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0453-2 · Cited by 83 · see citing works
Unsupervised Representation Learning by Predicting Random Distances
Hu, W., Pang, G., Shen, C., & Ma, C. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/408 · Cited by 72 · see citing works
Self-citationMining outlying aspects on numeric data
Duan, L., Tang, G., Pei, J., Bailey, J., Campbell, A., & Tang, C. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0398-2 · Cited by 70 · see citing works
Pang, G., Cao, L., Chen, L., Lian, D., & Liu, H. (2018) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v32i1.11692 · Cited by 59 · see citing works
Self-citationBeyond Outlier Detection: LookOut for Pictorial Explanation
Gupta, N., Eswaran, D., Shah, N., Akoglu, L., & Faloutsos, C. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-10925-7_8 · Cited by 56 · see citing works
Pang, G., Cao, L., Chen, L., & Liu, H. (2017) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2017/360 · Cited by 45 · see citing works
Self-citationPang, G., Cao, L., Chen, L., & Liu, H. (2016) · 2016 IEEE 16th International Conference on Data Mining (ICDM) · doi:10.1109/icdm.2016.0052 · Cited by 43 · see citing works
Self-citationBeyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation Network
Xu, H., Wang, Y., Jian, S., Huang, Z., Wang, Y., Liu, N., & Li, F. (2021) · International World Wide Web Conference (WWW) · doi:10.1145/3442381.3449868 · Cited by 40 · see citing works
Outlying property detection with numerical attributes
Angiulli, F., Fassetti, F., Manco, G., & Palopoli, L. (2017) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-016-0458-x · Cited by 33 · see citing works
Harnessing the Power of GPUs to Speed Up Feature Selection for Outlier Detection
Azmandian, F., Yılmazer, A., Dy, J., et al. (2014) · Journal of Computer Science and Technology · doi:10.1007/s11390-014-1439-4 · Cited by 11 · see citing works
LP-Explain: Local Pictorial Explanation for Outliers
Liu, H., Ma, F., Wang, Y., He, S., Chen, J., & Gao, J. (2020) · Proceedings · doi:10.1109/icdm50108.2020.00046 · Cited by 8 · see citing works
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A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139 · see citing works
“One natural question is, how can we trust these systems without understanding and validating the underlying rationale of the involved anomaly detection components? For this reason, XAD aims to not only provide accurate anomaly detection results, but also to provide tangible explanations of why a specific object is det…”
Background
- Authors
- Pang, G., & Aggarwal, C.
- Affiliations
- The University of Adelaide (AU)
- IBM T. J. Watson Research Center, New York, NY, USA
- ORCID
- Pang, Guansong
- Venue
- Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
- Publisher
- ACM
- Published
- 2021-08-14
- Pages
- 4056-4057
- OpenAlex
- W3195225944
- Cited by
- 23
- References
- 26
- Citations by year
- 2025: 4 · 2024: 10 · 2023: 6 · 2022: 2 · 2021: 1
- FWCI
- 2.19
- Type
- conference paper
- Language
- en
- Open access
- Locations
- ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) (publishedVersion)
- Singapore Management University Institutional Knowledge (InK) (Singapore Management University) (submittedVersion, other-oa)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Keywords
- anomaly detection, anomaly explanation, anomaly localization, explainable deep learning, black-box models, anomaly score, low-dimensional representation, interpretability
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