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

Showing 5175 of 703 papers

TitleStatusHype
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Picket: Guarding Against Corrupted Data in Tabular Data during Learning and InferenceCode1
Probabilistic AutoencoderCode1
Deep Clustering based Fair Outlier DetectionCode1
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier DetectionCode1
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory ReplayCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
Energy-based Detection of Adverse Weather Effects in LiDAR DataCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
Explainable Deep One-Class ClassificationCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
FaceMap: Towards Unsupervised Face Clustering via Map EquationCode1
Autoencoding Under Normalization ConstraintsCode1
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular BallsCode1
LSTM Fully Convolutional Networks for Time Series ClassificationCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Automating Outlier Detection via Meta-LearningCode1
Outlier detection in multivariate functional data through a contaminated mixture modelCode1
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98Unverified
2F-t ALSTM-FCNAccuracy0.95Unverified
3GENDISAccuracy0.94Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74Unverified