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
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural NetworksCode1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
Testing for Outliers with Conformal p-valuesCode1
NEAR - Newborns EEG Artifact RemovalCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
Autoencoding Under Normalization ConstraintsCode1
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
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
Coniferest: a complete active anomaly detection frameworkCode1
Deep Clustering based Fair Outlier DetectionCode1
Deep SetsCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
Explainable Deep One-Class ClassificationCode1
Explaining Anomalies Detected by Autoencoders Using SHAPCode1
FaceMap: Towards Unsupervised Face Clustering via Map EquationCode1
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
kTrans: Knowledge-Aware Transformer for Binary Code EmbeddingCode1
Automating Outlier Detection via Meta-LearningCode1
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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