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 2650 of 703 papers

TitleStatusHype
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural NetworksCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Coniferest: a complete active anomaly detection frameworkCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
NEAR - Newborns EEG Artifact RemovalCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
COPOD: Copula-Based Outlier DetectionCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
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
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
Autoencoding Under Normalization ConstraintsCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Explainable outlier detection through decision tree conditioningCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic DataCode1
Automating Outlier Detection via Meta-LearningCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
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
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Show:102550
← PrevPage 2 of 29Next →

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