SOTAVerified

Outlier Detection

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.

Source: Coverage-based Outlier Explanation

Papers

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Showing 1–50 of 703 papers

TitleStatusHype
ADBench: Anomaly Detection BenchmarkCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
Towards Total Recall in Industrial Anomaly DetectionCode2
Interactive Continual Learning: Fast and Slow ThinkingCode2
TODS: An Automated Time Series Outlier Detection SystemCode2
On Using Classification Datasets to Evaluate Graph-Level Outlier Detection: Peculiar Observations and New InsightsCode1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with OutliersCode1
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural NetworksCode1
Testing for Outliers with Conformal p-valuesCode1
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structuresCode1
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic DataCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Deep SetsCode1
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
FaceMap: Towards Unsupervised Face Clustering via Map EquationCode1
Automating Outlier Detection via Meta-LearningCode1
Handcrafted Outlier Detection RevisitedCode1
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier DetectionCode1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
LSTM Fully Convolutional Networks for Time Series ClassificationCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Multidimensional Uncertainty-Aware Evidential Neural NetworksCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
NEAR - Newborns EEG Artifact RemovalCode1
COPOD: Copula-Based Outlier DetectionCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised LearningCode1
Deep Clustering based Fair Outlier DetectionCode1
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier DetectionCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Explainable Deep One-Class ClassificationCode1
Explainable outlier detection through decision tree conditioningCode1
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
Autoencoding Under Normalization ConstraintsCode1
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular BallsCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
kTrans: Knowledge-Aware Transformer for Binary Code EmbeddingCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Learning on Graphs with Out-of-Distribution NodesCode1
Coniferest: a complete active anomaly detection frameworkCode1
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified