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
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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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