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 125 of 703 papers

TitleStatusHype
ADBench: Anomaly Detection BenchmarkCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
TODS: An Automated Time Series Outlier Detection SystemCode2
Interactive Continual Learning: Fast and Slow ThinkingCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
Towards Total Recall in Industrial Anomaly DetectionCode2
Energy-based Detection of Adverse Weather Effects in LiDAR DataCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Explainable Deep One-Class ClassificationCode1
COPOD: Copula-Based Outlier DetectionCode1
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier DetectionCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised LearningCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Autoencoding Under Normalization ConstraintsCode1
Coniferest: a complete active anomaly detection frameworkCode1
Deep Clustering based Fair Outlier DetectionCode1
Deep SetsCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
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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