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Given a long list of anomaly detection algorithms developed in the last few decades, how do they perform with regard to (i) varying levels of supervision, (ii) different types of anomalies, and (iii) noisy and corrupted data? In this work, we answer these key questions by conducting (to our best knowledge) the most comprehensive anomaly detection benchmark with 30 algorithms on 57 benchmark datasets, named ADBench.
The perceptron: a probabilistic model for information storage and organization in the brain
F. Rosenblatt · 1958
Earlier work this paper cites.
Computer-intensive methods in statistics
P. Diaconis and B. Efron · 1983
Earlier work this paper cites.
An algorithm for generating artificial test clusters
G. W. Milligan · 1985
Earlier work this paper cites.
Induction of decision trees
J. R. Quinlan · 1986
Earlier work this paper cites.
A knowledge-elicitation tool for sophisticated users
B. Cestnik, I. Kononenko, and I. Bratko · 1987
Earlier work this paper cites.
Inductive knowledge acquisition: a case study
J. R. Quinlan, P. J. Compton, K. Horn, and L. Lazarus · 1987
Earlier work this paper cites.
Rule induction in forensic science
I. W. Evett and E. J. Spiehler · 1989
Earlier work this paper cites.
Classification of radar returns from the ionosphere using neural networks
V. G. Sigillito, S. P. Wing, L. V. Hutton, and K. B. Baker · 1989
Earlier work this paper cites.
Multisurface method of pattern separation for medical diagnosis applied to breast cytology
W. H. Wolberg and O. L. Mangasarian · 1990
Earlier work this paper cites.
Letter recognition using holland-style adaptive classifiers
P. W. Frey and D. J. Slate · 1991
Earlier work this paper cites.
The classification performance of rda
S. Aeberhard, D. Coomans, and O. de Vel · 1992
Earlier work this paper cites.
Stacked generalization
D. H. Wolpert · 1992
Earlier work this paper cites.
A comparison of dynamic reposing and tangent distance for drug activity prediction
T. Dietterich, A. Jain, R. Lathrop, and T. Lozano-Perez · 1993
Earlier work this paper cites.
Comparative evaluation of pattern recognition techniques for detection of microcalcifications in mammography
K. S. Woods, J. L. Solka, C. E. Priebe, W. P. Kegelmeyer Jr, C. C. Doss, and K. W. Bowyer · 1994
Earlier work this paper cites.
Support vector machine
C. Cortes and V. Vapnik · 1995
Earlier work this paper cites.
Newsweeder: Learning to filter netnews
K. Lang · 1995
Earlier work this paper cites.
Breast cancer diagnosis and prognosis via linear programming
O. L. Mangasarian, W. N. Street, and W. H. Wolberg · 1995
Earlier work this paper cites.
Methods of combining multiple classifiers based on different representations for pen-based handwritten digit recognition
F. Alimoglu and E. Alpaydin · 1996
Earlier work this paper cites.
A probabilistic classification system for predicting the cellular localization sites of proteins
P. Horton and K. Nakai · 1996
Earlier work this paper cites.
A further comparison of simplification methods for decision-tree induction
D. Malerba, F. Esposito, and G. Semeraro · 1996
Earlier work this paper cites.
No free lunch theorems for optimization
D. H. Wolpert and W. G. Macready · 1997
Earlier work this paper cites.
Cascading classifiers
E. Alpaydin and C. Kaynak · 1998
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Comparative accuracies of artificial neural networks and discriminant analysis in predicting forest cover types from cartographic variables
J. A. Blackard and D. J. Dean · 1999
Earlier work this paper cites.
Multidimensional curve classification using passing-through regions
M. Kudo, J. Toyama, and M. Shimbo · 1999
Earlier work this paper cites.
Support vector method for novelty detection
B. Schölkopf, R. C. Williamson, A. J. Smola, J. Shawe-Taylor, J. C. Platt, et al · 1999
Earlier work this paper cites.
Sisporto 2.0: a program for automated analysis of cardiotocograms
D. Ayres-de Campos, J. Bernardes, A. Garrido, J. Marques-de Sa, and L. Pereira-Leite · 2000
Earlier work this paper cites.
Lof: identifying density-based local outliers
M. M. Breunig, H.-P. Kriegel, R. T. Ng, and J. Sander · 2000
Earlier work this paper cites.
Efficient algorithms for mining outliers from large data sets
S. Ramaswamy, R. Rastogi, and K. Shim · 2000
Earlier work this paper cites.
Random forests
L. Breiman · 2001
Earlier work this paper cites.
Fast outlier detection in high dimensional spaces
F. Angiulli and C. Pizzuti · 2002
Earlier work this paper cites.
Enhancing effectiveness of outlier detections for low density patterns
J. Tang, Z. Chen, A. W.-C. Fu, and D. W. Cheung · 2002
Earlier work this paper cites.
Discovering cluster-based local outliers
Z. He, X. Xu, and S. Deng · 2003
Earlier work this paper cites.
A comparative study of anomaly detection schemes in network intrusion detection
A. Lazarevic, L. Ertoz, V. Kumar, A. Ozgur, and J. Srivastava · 2003
Earlier work this paper cites.
Novelty detection: a review—part 1: statistical approaches
M. Markou and S. Singh · 2003
Earlier work this paper cites.
A novel anomaly detection scheme based on principal component classifier
M.-L. Shyu, S.-C. Chen, K. Sarinnapakorn, and L. Chang · 2003
Earlier work this paper cites.
Analysis of the sagittal balance of the spine and pelvis using shape and orientation parameters
E. Berthonnaud, J. Dimnet, P. Roussouly, and H. Labelle · 2005
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
Earlier work this paper cites.
Isolation forest
F. T. Liu, K. M. Ting, and Z.-H. Zhou · 2008
Earlier work this paper cites.
Visualizing data using t-sne
L. Van der Maaten and G. Hinton · 2008
Earlier work this paper cites.
Pair-copula constructions of multiple dependence
K. Aas, C. Czado, A. Frigessi, and H. Bakken · 2009
Earlier work this paper cites.
Imagenet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
Earlier work this paper cites.
The elements of statistical learning: data mining, inference, and prediction
T. Hastie, R. Tibshirani, J. H. Friedman, and J. H. Friedman · 2009
Earlier work this paper cites.
Outlier detection in axis-parallel subspaces of high dimensional data
H.-P. Kriegel, P. Kröger, E. Schubert, and A. Zimek · 2009
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Classification and regression trees
W.-Y. Loh · 2011
Earlier work this paper cites.
Learning word vectors for sentiment analysis
A. L. Maas, R. E. Daly, P. T. Pham, D. Huang, A. Y. Ng, and C. Potts · 2011
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in python
F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. Grisel, M. Blondel, P. Prettenhofer, R. Weiss, V. Dubourg, et al · 2011
Earlier work this paper cites.
Description and analysis of the brno276 system for lre2011
N. Brümmer, S. Cumani, O. Glembek, M. Karafiát, P. Matějka, J. Pešán, O. Plchot, M. Soufifar, E. d. Villiers, and J. H. Černockỳ · 2012
Earlier work this paper cites.
Histogram-based outlier score (hbos): A fast unsupervised anomaly detection algorithm
M. Goldstein and A. Dengel · 2012
Earlier work this paper cites.
Adadelta: an adaptive learning rate method
M. D. Zeiler · 2012
Earlier work this paper cites.
Toward supervised anomaly detection
N. Görnitz, M. Kloft, K. Rieck, and U. Brefeld · 2013
Earlier work this paper cites.
Physics-based anomaly detection defined on manifold space
H. Huang, H. Qin, S. Yoo, and D. Yu · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Glove: Global vectors for word representation
J. Pennington, R. Socher, and C. D. Manning · 2014
Earlier work this paper cites.
A review of novelty detection
M. A. Pimentel, D. A. Clifton, L. Clifton, and L. Tarassenko · 2014
Earlier work this paper cites.
A meta-analysis of the anomaly detection problem
A. Emmott, S. Das, T. Dietterich, A. Fern, and W.-K. Wong · 2015
Earlier work this paper cites.
Evaluating real-time anomaly detection algorithms–the numenta anomaly benchmark
A. Lavin and S. Ahmad · 2015
Earlier work this paper cites.
Deep learning
Y. LeCun, Y. Bengio, and G. Hinton · 2015
Earlier work this paper cites.
Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set)
N. Moustafa and J. Slay · 2015
Earlier work this paper cites.
Character-level convolutional networks for text classification
X. Zhang, J. Zhao, and Y. LeCun · 2015
Earlier work this paper cites.
Semi-supervised statistical approach for network anomaly detection
N. B. Aissa and M. Guerroumi · 2016
Earlier work this paper cites.
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
G. O. Campos, A. Zimek, J. Sander, R. J. Campello, B. Micenková, E. Schubert, I. Assent, and M. E. Houle · 2016
Earlier work this paper cites.
Xgboost: A scalable tree boosting system
T. Chen and C. Guestrin · 2016
Earlier work this paper cites.
A comparative evaluation of unsupervised anomaly detection algorithms for multivariate data
M. Goldstein and S. Uchida · 2016
Earlier work this paper cites.
Deep learning
I. Goodfellow, Y. Bengio, and A. Courville · 2016
Earlier work this paper cites.
Robust random cut forest based anomaly detection on streams
S. Guha, N. Mishra, G. Roy, and O. Schrijvers · 2016
Earlier work this paper cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Ups and downs: Modeling the visual evolution of fashion trends with one-class collaborative filtering
R. He and J. McAuley · 2016
Cited alongside, same era.
Fault detection and diagnosis in nonlinear systems
R. Martinez-Guerra and J. L. Mata-Machuca · 2016
Cited alongside, same era.
Unsupervised feature selection for outlier detection by modelling hierarchical value-feature couplings
G. Pang, L. Cao, L. Chen, and H. Liu · 2016
Cited alongside, same era.
Loda: Lightweight on-line detector of anomalies
T. Pevnỳ · 2016
Cited alongside, same era.
ODDS library, 2016
S. Rayana · 2016
Cited alongside, same era.
Anomaly detection with domain adaptation
Z. Yang, I. S. Bozchalooi, and E. Darve · 2020
Later among the works it cites.
Meta-aad: Active anomaly detection with deep reinforcement learning
D. Zha, K.-H. Lai, M. Wan, and X. Hu · 2020
Later among the works it cites.
Multi-attributed heterogeneous graph convolutional network for bot detection
J. Zhao, X. Liu, Q. Yan, B. Li, M. Shao, and H. Peng · 2020
Later among the works it cites.
Deep neural networks and tabular data: A survey
V. Borisov, T. Leemann, K. Seßler, J. Haug, M. Pawelczyk, and G. Kasneci · 2021
Later among the works it cites.
Learned robust pca: A scalable deep unfolding approach for high-dimensional outlier detection
H. Cai, J. Liu, and W. Yin · 2021
Later among the works it cites.
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An overview of gradient descent optimization algorithms
S. Ruder · 2016
Cited alongside, same era.
An introduction to outlier analysis
C. C. Aggarwal · 2017
Cited alongside, same era.
Enriching word vectors with subword information
P. Bojanowski, E. Grave, A. Joulin, and T. Mikolov · 2017
Cited alongside, same era.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
D. Hendrycks and K. Gimpel · 2017
Cited alongside, same era.
Bag of tricks for efficient text classification
A. Joulin, É. Grave, P. Bojanowski, and T. Mikolov · 2017
Cited alongside, same era.
Lightgbm: A highly efficient gradient boosting decision tree
G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu · 2017
Cited alongside, same era.
Segmentmeifyoucan: A benchmark for anomaly segmentation
R. Chan, K. Lis, S. Uhlemeyer, H. Blum, S. Honari, R. Siegwart, P. Fua, M. Salzmann, and M. Rottmann · 2021
Later among the works it cites.
Transfer-based semantic anomaly detection
L. Deecke, L. Ruff, R. A. Vandermeulen, and H. Bilen · 2021
Later among the works it cites.
Learning diverse-structured networks for adversarial robustness
X. Du, J. Zhang, B. Han, T. Liu, Y. Rong, G. Niu, J. Huang, and M. Sugiyama · 2021
Later among the works it cites.
Re-thinking co-salient object detection
D.-P. Fan, T. Li, Z. Lin, G.-P. Ji, D. Zhang, M.-M. Cheng, H. Fu, and J. Shen · 2021
Later among the works it cites.
When does contrastive learning preserve adversarial robustness from pretraining to finetuning?
L. Fan, S. Liu, P.-Y. Chen, G. Zhang, and C. Gan · 2021
Later among the works it cites.
Mimosa: Multi-constraint molecule sampling for molecule optimization
T. Fu, C. Xiao, X. Li, L. M. Glass, and J. Sun · 2021
Later among the works it cites.
Revisiting deep learning models for tabular data
Y. Gorishniy, I. Rubachev, V. Khrulkov, and A. Babenko · 2021
Later among the works it cites.
Uncovering the source of machine bias
X. Hu, Y. Huang, B. Li, and T. Lu · 2021
Later among the works it cites.
Therapeutics data commons: Machine learning datasets and tasks for drug discovery and development
K. Huang, T. Fu, W. Gao, Y. Zhao, Y. Roohani, J. Leskovec, C. Coley, C. Xiao, J. Sun, and M. Zitnik · 2021
Later among the works it cites.
Opengan: Open-set recognition via open data generation
S. Kong and D. Ramanan · 2021
Later among the works it cites.
Tods: An automated time series outlier detection system
K.-H. Lai, D. Zha, G. Wang, J. Xu, Y. Zhao, D. Kumar, Y. Chen, P. Zumkhawaka, M. Wan, D. Martinez, et al · 2021
Later among the works it cites.
Revisiting time series outlier detection: Definitions and benchmarks
K.-H. Lai, D. Zha, J. Xu, Y. Zhao, G. Wang, and X. Hu · 2021
Later among the works it cites.
Gen 2 out: Detecting and ranking generalized anomalies
M.-C. Lee, S. Shekhar, C. Faloutsos, T. N. Hutson, and L. Iasemidis · 2021
Later among the works it cites.
Event outlier detection in continuous time
S. Liu and M. Hauskrecht · 2021
Later among the works it cites.
A large-scale study on unsupervised outlier model selection: Do internal strategies suffice?
M. Q. Ma, Y. Zhao, X. Zhang, and L. Akoglu · 2021
Later among the works it cites.
A comprehensive survey on graph anomaly detection with deep learning
X. Ma, J. Wu, S. Xue, J. Yang, C. Zhou, Q. Z. Sheng, H. Xiong, and L. Akoglu · 2021
Later among the works it cites.
A survey on open set recognition
A. Mahdavi and M. Carvalho · 2021
Later among the works it cites.
Date: Detecting anomalies in text via self-supervision of transformers
A. Manolache, F. Brad, and E. Burceanu · 2021
Later among the works it cites.
Toward explainable deep anomaly detection
G. Pang and C. Aggarwal · 2021
Later among the works it cites.
Homophily outlier detection in non-iid categorical data
G. Pang, L. Cao, and L. Chen · 2021
Later among the works it cites.
Explainable deep few-shot anomaly detection with deviation networks
G. Pang, C. Ding, C. Shen, and A. v. d. Hengel · 2021
Later among the works it cites.
Deep learning for anomaly detection: A review
G. Pang, C. Shen, L. Cao, and A. V. D. Hengel · 2021
Later among the works it cites.
Neural transformation learning for deep anomaly detection beyond images
C. Qiu, T. Pfrommer, M. Kloft, S. Mandt, and M. Rudolph · 2021
Later among the works it cites.
An information retrieval approach to building datasets for hate speech detection
M. M. Rahman, D. Balakrishnan, D. Murthy, M. Kutlu, and M. Lease · 2021
Later among the works it cites.
Online false discovery rate control for anomaly detection in time series
Q. Rebjock, B. Kurt, T. Januschowski, and L. Callot · 2021
Later among the works it cites.
Panda: Adapting pretrained features for anomaly detection and segmentation
T. Reiss, N. Cohen, L. Bergman, and Y. Hoshen · 2021
Later among the works it cites.
A unifying review of deep and shallow anomaly detection
L. Ruff, J. R. Kauffmann, R. A. Vandermeulen, G. Montavon, W. Samek, M. Kloft, T. G. Dietterich, and K.-R. Müller · 2021
Later among the works it cites.
M. Salehi, H. Mirzaei, D. Hendrycks, Y. Li, M. H. Rohban, and M. Sabokrou · 2021
Later among the works it cites.
Fairod: Fairness-aware outlier detection
S. Shekhar, N. Shah, and L. Akoglu · 2021
Later among the works it cites.
Anomaly detection for tabular data with internal contrastive learning
T. Shenkar and L. Wolf · 2021
Later among the works it cites.
The effect of hyperparameter tuning on the comparative evaluation of unsupervised anomaly detection methods
J. Soenen, E. Van Wolputte, L. Perini, V. Vercruyssen, W. Meert, J. Davis, and H. Blockeel · 2021
Later among the works it cites.
Deep clustering based fair outlier detection
H. Song, P. Li, and H. Liu · 2021
Later among the works it cites.
Benchmarking unsupervised outlier detection with realistic synthetic data
G. Steinbuss and K. Böhm · 2021
Later among the works it cites.
Rade: Resource-efficient supervised anomaly detection using decision tree-based ensemble methods
S. Vargaftik, I. Keslassy, A. Orda, and Y. Ben-Itzhak · 2021
Later among the works it cites.
Do wider neural networks really help adversarial robustness?
B. Wu, J. Chen, D. Cai, X. He, and Q. Gu · 2021
Later among the works it cites.
Do we really need to learn representations from in-domain data for outlier detection?
Z. Xiao, Q. Yan, and Y. Amit · 2021
Later among the works it cites.
Beyond outlier detection: Outlier interpretation by attention-guided triplet deviation network
H. Xu, Y. Wang, S. Jian, Z. Huang, Y. Wang, N. Liu, and F. Li · 2021
Later among the works it cites.
Dp-ssl: Towards robust semi-supervised learning with a few labeled samples
Y. Xu, J. Ding, L. Zhang, and S. Zhou · 2021
Later among the works it cites.
Generalized out-of-distribution detection: A survey
J. Yang, K. Zhou, Y. Li, and Z. Liu · 2021
Later among the works it cites.
Towards fair deep anomaly detection
H. Zhang and I. Davidson · 2021
Later among the works it cites.
Suod: Accelerating large-scale unsupervised heterogeneous outlier detection
Y. Zhao, X. Hu, C. Cheng, C. Wang, C. Wan, W. Wang, J. Yang, H. Bai, Z. Li, C. Xiao, et al · 2021
Later among the works it cites.
Automatic unsupervised outlier model selection
Y. Zhao, R. Rossi, and L. Akoglu · 2021
Later among the works it cites.
Meta label correction for noisy label learning
G. Zheng, A. H. Awadallah, and S. Dumais · 2021
Later among the works it cites.
Learning placeholders for open-set recognition
D.-W. Zhou, H.-J. Ye, and D.-C. Zhan · 2021
Later among the works it cites.
Feature encoding with autoencoders for weakly supervised anomaly detection
Y. Zhou, X. Song, Y. Zhang, F. Liu, C. Zhu, and L. Liu · 2021
Later among the works it cites.
Anomalib: A deep learning library for anomaly detection
S. Akcay, D. Ameln, A. Vaidya, B. Lakshmanan, N. Ahuja, and U. Genc · 2022
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Unsupervised anomaly detection by robust density estimation
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Latent outlier exposure for anomaly detection with contaminated data
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