{"url":"/task/outlier-detection","name":"Outlier Detection","slug":"outlier-detection","description_markdown":"**Outlier Detection** is a task of identifying a subset of a given data set which are considered anomalous in that they are unusual from other instances. It is one of the core data mining tasks and is central to many applications. In the security field, it can be used to identify potentially threatening users, in the manufacturing field it can be used to identify parts that are likely to fail.\n\n\n<span class=\"description-source\">Source: [Coverage-based Outlier Explanation ](https://arxiv.org/abs/1911.02617)</span>","categories":[{"name":"Graphs","url":"/area/graphs"},{"name":"Methodology","url":"/area/methodology"}],"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","slug_source":"archive_url"},"counts":{"papers_tagged":703,"papers_with_code":234,"benchmarks":11,"benchmark_tables_in_archive":11,"benchmark_tables_shown":11,"benchmark_tables_withheld_as_spam":0,"benchmark_definition":"a leaderboard table with at least one row; benchmark_tables_shown also counts the zero-row tables; benchmark_tables_in_archive adds the tables withheld as spam","datasets":11,"subtasks":4,"parent_tasks":0},"benchmarks":[{"leaderboard":"/sota/outlier-detection-on-ecg5000","slug":"outlier-detection-on-ecg5000","dataset":"ECG5000","dataset_url":"/dataset/ecg5000","rows_in_archive":3,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"VRAE+SVM","paper_title":"Learning Representations from Healthcare Time Series Data for Unsupervised Anomaly Detection","paper_url":"/paper/learning-representations-from-healthcare-time","paper_date":"2019-04-04","arxiv_id":null,"code_links":[],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-balance-scale_class-1","slug":"outlier-detection-on-balance-scale_class-1","dataset":"Balance scale_class 1","dataset_url":null,"rows_in_archive":1,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"ASVDD","paper_title":"Automatic support vector data description","paper_url":"/paper/automatic-support-vector-data-description","paper_date":"2018-01-01","arxiv_id":null,"code_links":[{"title":"RezaSadeghiWSU/ASVDD","url":"https://github.com/RezaSadeghiWSU/ASVDD"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-breast-cancer","slug":"outlier-detection-on-breast-cancer","dataset":"Breast cancer Wisconsin_class 2","dataset_url":null,"rows_in_archive":1,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"ASVDD","paper_title":"Automatic support vector data description","paper_url":"/paper/automatic-support-vector-data-description","paper_date":"2018-01-01","arxiv_id":null,"code_links":[{"title":"RezaSadeghiWSU/ASVDD","url":"https://github.com/RezaSadeghiWSU/ASVDD"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-breast-cancer-1","slug":"outlier-detection-on-breast-cancer-1","dataset":"Breast cancer Wisconsin_class 4","dataset_url":null,"rows_in_archive":1,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"ASVDD","paper_title":"Automatic support vector data description","paper_url":"/paper/automatic-support-vector-data-description","paper_date":"2018-01-01","arxiv_id":null,"code_links":[{"title":"RezaSadeghiWSU/ASVDD","url":"https://github.com/RezaSadeghiWSU/ASVDD"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-fashion-mnist","slug":"outlier-detection-on-fashion-mnist","dataset":"Fashion-MNIST","dataset_url":"/dataset/fashion-mnist","rows_in_archive":1,"metrics":["AUROC"],"first_row_in_archive_order":{"model":"PAE","paper_title":"Probabilistic Autoencoder","paper_url":"/paper/probabilistic-auto-encoder","paper_date":"2020-06-09","arxiv_id":"2006.05479","code_links":[{"title":"VMBoehm/PAE","url":"https://github.com/VMBoehm/PAE"},{"title":"chrvt/denoising-normalizing-flow","url":"https://github.com/chrvt/denoising-normalizing-flow"},{"title":"AI-for-Ocean-Science/ulmo","url":"https://github.com/AI-for-Ocean-Science/ulmo"},{"title":"vmboehm/pae-ablation","url":"https://github.com/vmboehm/pae-ablation"}],"syntology":{"n":13,"n_ran":1,"n_unverified":12,"n_pointer_only":0}}},{"leaderboard":"/sota/outlier-detection-on-glass-identification","slug":"outlier-detection-on-glass-identification","dataset":"Glass identification","dataset_url":"/dataset/glass","rows_in_archive":1,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"ASVDD","paper_title":"Automatic support vector data description","paper_url":"/paper/automatic-support-vector-data-description","paper_date":"2018-01-01","arxiv_id":null,"code_links":[{"title":"RezaSadeghiWSU/ASVDD","url":"https://github.com/RezaSadeghiWSU/ASVDD"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-heart-c","slug":"outlier-detection-on-heart-c","dataset":"Heart-C","dataset_url":null,"rows_in_archive":1,"metrics":["AUC"],"first_row_in_archive_order":{"model":"MIX","paper_title":"MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data","paper_url":"/paper/mix-a-joint-learning-framework-for-detecting","paper_date":"2019-11-01","arxiv_id":null,"code_links":[{"title":"xuhongzuo/MIX","url":"https://github.com/xuhongzuo/MIX"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-hepatitis","slug":"outlier-detection-on-hepatitis","dataset":"Hepatitis","dataset_url":null,"rows_in_archive":1,"metrics":["AUC-ROC"],"first_row_in_archive_order":{"model":"MIX","paper_title":"MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data","paper_url":"/paper/mix-a-joint-learning-framework-for-detecting","paper_date":"2019-11-01","arxiv_id":null,"code_links":[{"title":"xuhongzuo/MIX","url":"https://github.com/xuhongzuo/MIX"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-internet-ad","slug":"outlier-detection-on-internet-ad","dataset":"Internet Ad","dataset_url":null,"rows_in_archive":1,"metrics":["AUC-ROC"],"first_row_in_archive_order":{"model":"MIX","paper_title":"MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type Data","paper_url":"/paper/mix-a-joint-learning-framework-for-detecting","paper_date":"2019-11-01","arxiv_id":null,"code_links":[{"title":"xuhongzuo/MIX","url":"https://github.com/xuhongzuo/MIX"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-ionosphere_class-b","slug":"outlier-detection-on-ionosphere_class-b","dataset":"Ionosphere_class b","dataset_url":null,"rows_in_archive":1,"metrics":["Average Accuracy"],"first_row_in_archive_order":{"model":"ASVDD","paper_title":"Automatic support vector data description","paper_url":"/paper/automatic-support-vector-data-description","paper_date":"2018-01-01","arxiv_id":null,"code_links":[{"title":"RezaSadeghiWSU/ASVDD","url":"https://github.com/RezaSadeghiWSU/ASVDD"}],"syntology":null}},{"leaderboard":"/sota/outlier-detection-on-skab","slug":"outlier-detection-on-skab","dataset":"SKAB","dataset_url":"/dataset/skab","rows_in_archive":1,"metrics":["Average F1"],"first_row_in_archive_order":{"model":"LSTMCaps","paper_title":"Hybridization of Capsule and LSTM Networks for unsupervised anomaly detection on multivariate data","paper_url":"/paper/hybridization-of-capsule-and-lstm-networks","paper_date":"2022-02-11","arxiv_id":"2202.05538","code_links":[],"syntology":null}}],"datasets":[{"url":"/dataset/fashion-mnist","name":"Fashion-MNIST","full_name":"","num_papers_in_archive":3202},{"url":"/dataset/mvtecad","name":"MVTecAD","full_name":"MVTEC ANOMALY DETECTION DATASET","num_papers_in_archive":402},{"url":"/dataset/imagenet-o","name":"ImageNet-O","full_name":"","num_papers_in_archive":89},{"url":"/dataset/pathbased","name":"pathbased","full_name":"","num_papers_in_archive":25},{"url":"/dataset/ecoli","name":"Ecoli","full_name":null,"num_papers_in_archive":9},{"url":"/dataset/ecg5000","name":"ECG5000","full_name":"ECG5000","num_papers_in_archive":6},{"url":"/dataset/epinion","name":"Epinion","full_name":null,"num_papers_in_archive":5},{"url":"/dataset/wikisem500","name":"WikiSem500","full_name":null,"num_papers_in_archive":4},{"url":"/dataset/skab","name":"SKAB","full_name":"Skoltech Anomaly Benchmark","num_papers_in_archive":3},{"url":"/dataset/vistas-np","name":"Vistas-NP","full_name":null,"num_papers_in_archive":2},{"url":"/dataset/adfi-dataset-anomaly-detection-dataset","name":"ADFI","full_name":"Anomaly Detection Datasets for Visual Inspection","num_papers_in_archive":0}],"subtasks":[{"url":"/task/graph-outlier-detection","name":"Graph Outlier Detection"},{"url":"/task/one-class-classifier","name":"One-class classifier"},{"url":"/task/outlier-ensembles","name":"outlier ensembles"},{"url":"/task/outlier-interpretation","name":"Outlier Interpretation"}],"parent_tasks":[],"papers":{"order":"repositories listed in the archive (desc), then date (desc); the archive holds no stars","population":"papers tagged with this task that list at least one repository in the archive","shown":30,"of":234,"tagged_in_all":703,"items":[{"url":"/paper/towards-total-recall-in-industrial-anomaly","title":"Towards Total Recall in Industrial Anomaly Detection","date":"2021-06-15","arxiv_id":"2106.08265","repositories_listed":18,"syntology":{"n":36,"n_ran":5,"n_unverified":31,"n_pointer_only":0}},{"url":"/paper/lstm-fully-convolutional-networks-for-time","title":"LSTM Fully Convolutional Networks for Time Series Classification","date":"2017-09-08","arxiv_id":"1709.05206","repositories_listed":9,"syntology":{"n":2,"n_ran":2,"n_unverified":0,"n_pointer_only":2}},{"url":"/paper/lstm-based-encoder-decoder-for-multi-sensor","title":"LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection","date":"2016-07-01","arxiv_id":"1607.00148","repositories_listed":8,"syntology":{"n":9,"n_ran":1,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/deep-semi-supervised-anomaly-detection","title":"Deep Semi-Supervised Anomaly Detection","date":"2019-06-06","arxiv_id":"1906.02694","repositories_listed":7,"syntology":{"n":2,"n_ran":0,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/deep-sets","title":"Deep Sets","date":"2017-03-10","arxiv_id":"1703.06114","repositories_listed":7,"syntology":{"n":12,"n_ran":9,"n_unverified":3,"n_pointer_only":9}},{"url":"/paper/adbench-anomaly-detection-benchmark","title":"ADBench: Anomaly Detection Benchmark","date":"2022-06-19","arxiv_id":"2206.09426","repositories_listed":5,"syntology":{"n":1,"n_ran":0,"n_unverified":1,"n_pointer_only":0}},{"url":"/paper/adversarially-learned-one-class-classifier","title":"Adversarially Learned One-Class Classifier for Novelty Detection","date":"2018-02-25","arxiv_id":"1802.09088","repositories_listed":5,"syntology":null},{"url":"/paper/generalized-out-of-distribution-detection-a","title":"Generalized Out-of-Distribution Detection: A Survey","date":"2021-10-21","arxiv_id":"2110.11334","repositories_listed":4,"syntology":null},{"url":"/paper/probabilistic-auto-encoder","title":"Probabilistic Autoencoder","date":"2020-06-09","arxiv_id":"2006.05479","repositories_listed":4,"syntology":{"n":13,"n_ran":1,"n_unverified":12,"n_pointer_only":0}},{"url":"/paper/using-self-supervised-learning-can-improve","title":"Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty","date":"2019-06-28","arxiv_id":"1906.12340","repositories_listed":4,"syntology":{"n":12,"n_ran":5,"n_unverified":7,"n_pointer_only":0}},{"url":"/paper/pyod-a-python-toolbox-for-scalable-outlier","title":"PyOD: A Python Toolbox for Scalable Outlier Detection","date":"2019-01-06","arxiv_id":"1901.01588","repositories_listed":4,"syntology":{"n":4,"n_ran":0,"n_unverified":4,"n_pointer_only":0}},{"url":"/paper/geometry-and-accuracy-preserving-random","title":"Geometry- and Accuracy-Preserving Random Forest Proximities","date":"2022-01-29","arxiv_id":"2201.12682","repositories_listed":3,"syntology":null},{"url":"/paper/ssd-a-unified-framework-for-self-supervised-1","title":"SSD: A Unified Framework for Self-Supervised Outlier Detection","date":"2021-03-22","arxiv_id":"2103.12051","repositories_listed":3,"syntology":{"n":1,"n_ran":1,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/copod-copula-based-outlier-detection","title":"COPOD: Copula-Based Outlier Detection","date":"2020-09-20","arxiv_id":"2009.09463","repositories_listed":3,"syntology":null},{"url":"/paper/adalam-revisiting-handcrafted-outlier","title":"AdaLAM: Revisiting Handcrafted Outlier Detection","date":"2020-06-07","arxiv_id":"2006.04250","repositories_listed":3,"syntology":{"n":19,"n_ran":0,"n_unverified":19,"n_pointer_only":0}},{"url":"/paper/an-overview-and-a-benchmark-of-active","title":"An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class Classifiers","date":"2018-08-14","arxiv_id":"1808.04759","repositories_listed":3,"syntology":null},{"url":"/paper/learning-representations-of-ultrahigh","title":"Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection","date":"2018-06-13","arxiv_id":"1806.04808","repositories_listed":3,"syntology":{"n":5,"n_ran":5,"n_unverified":0,"n_pointer_only":5}},{"url":"/paper/sliced-wasserstein-based-anomaly-detection","title":"Sliced-Wasserstein-based Anomaly Detection and Open Dataset for Localized Critical Peak Rebates","date":"2024-10-29","arxiv_id":"2410.21712","repositories_listed":2,"syntology":null},{"url":"/paper/rethinking-unsupervised-outlier-detection-via","title":"Rethinking Unsupervised Outlier Detection via Multiple Thresholding","date":"2024-07-07","arxiv_id":"2407.05382","repositories_listed":2,"syntology":{"n":4,"n_ran":4,"n_unverified":0,"n_pointer_only":0}},{"url":"/paper/gcpv-guided-concept-projection-vectors-for","title":"Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature Spaces","date":"2023-11-24","arxiv_id":"2311.14435","repositories_listed":2,"syntology":null},{"url":"/paper/learning-point-wise-abstaining-penalty-for","title":"LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic Data","date":"2023-09-19","arxiv_id":"2309.10230","repositories_listed":2,"syntology":null},{"url":"/paper/computationally-assisted-quality-control-for","title":"Computationally Assisted Quality Control for Public Health Data Streams","date":"2023-06-29","arxiv_id":"2306.16914","repositories_listed":2,"syntology":{"n":8,"n_ran":0,"n_unverified":8,"n_pointer_only":0}},{"url":"/paper/defense-mechanisms-against-training-hijacking","title":"SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier Detection","date":"2023-02-16","arxiv_id":"2302.08618","repositories_listed":2,"syntology":null},{"url":"/paper/benchmarking-node-outlier-detection-on-graphs","title":"BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs","date":"2022-06-21","arxiv_id":"2206.10071","repositories_listed":2,"syntology":null},{"url":"/paper/deepcave-an-interactive-analysis-tool-for","title":"DeepCAVE: An Interactive Analysis Tool for Automated Machine Learning","date":"2022-06-07","arxiv_id":"2206.03493","repositories_listed":2,"syntology":null},{"url":"/paper/ecod-unsupervised-outlier-detection-using","title":"ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions","date":"2022-01-02","arxiv_id":"2201.00382","repositories_listed":2,"syntology":null},{"url":"/paper/tod-tensor-based-outlier-detection","title":"TOD: GPU-accelerated Outlier Detection via Tensor Operations","date":"2021-10-26","arxiv_id":"2110.14007","repositories_listed":2,"syntology":{"n":10,"n_ran":0,"n_unverified":10,"n_pointer_only":0}},{"url":"/paper/learn-then-test-calibrating-predictive","title":"Learn then Test: Calibrating Predictive Algorithms to Achieve Risk Control","date":"2021-10-03","arxiv_id":"2110.01052","repositories_listed":2,"syntology":{"n":22,"n_ran":6,"n_unverified":16,"n_pointer_only":0}},{"url":"/paper/autoencoding-under-normalization-constraints","title":"Autoencoding Under Normalization Constraints","date":"2021-05-12","arxiv_id":"2105.05735","repositories_listed":2,"syntology":{"n":9,"n_ran":7,"n_unverified":2,"n_pointer_only":0}},{"url":"/paper/a-scene-agnostic-framework-with-adversarial","title":"A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video","date":"2020-08-27","arxiv_id":"2008.12328","repositories_listed":2,"syntology":{"n":3,"n_ran":3,"n_unverified":0,"n_pointer_only":3}}],"syntology_records":18,"syntology_note":"a paper without a record is not a recorded non-run: it may lack an arXiv id or simply be absent from the graph layer"},"description_links":{"kept":0,"unwrapped_to_text":0,"bare_urls_linked":0,"relative_images_dropped":0,"rule":"internal links are kept only when the target slug exists in the catalog"},"syntology":{"read_at":"2026-09-24T18:15:14+00:00","claim":"Per-sample execution status on synthesized fixtures ('ran N of M samples'); not a correctness claim and not a ranking signal.","status_vocabulary":{"ran_honours":"ran, honoured the contract we drafted","ran_violates":"ran, violated the contract we drafted","ran_draft_wrong":"ran; our contract draft was wrong, not the code","ran_fixture":"ran; our fixture could not drive it","ran":"ran on a synthesized input","unverified":"unverified (harvested, no recorded run)"}},"not_shown":{"libraries":"the archive has no per-task library table","trend_sparklines":"the Trend column of the benchmarks table was a rendered image; it is not in the archive","social_and_latest_sorts":"stars and social signals are not in the archive"}}