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

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Showing 1–50 of 703 papers

TitleStatusHype
ADBench: Anomaly Detection BenchmarkCode3
DeepCAVE: An Interactive Analysis Tool for Automated Machine LearningCode3
Interactive Continual Learning: Fast and Slow ThinkingCode2
Towards Total Recall in Industrial Anomaly DetectionCode2
TODS: An Automated Time Series Outlier Detection SystemCode2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly DetectionCode2
Out-of-Distribution Detection on Graphs: A SurveyCode1
Fuzzy Granule Density-Based Outlier Detection with Multi-Scale Granular BallsCode1
Coniferest: a complete active anomaly detection frameworkCode1
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory ReplayCode1
Rethinking Graph Backdoor Attacks: A Distribution-Preserving PerspectiveCode1
Outlier detection by ensembling uncertainty with negative objectnessCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic DataCode1
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
kTrans: Knowledge-Aware Transformer for Binary Code EmbeddingCode1
Learning on Graphs with Out-of-Distribution NodesCode1
Uncertainty Quantification for Image-based Traffic Prediction across CitiesCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structuresCode1
Energy-based Detection of Adverse Weather Effects in LiDAR DataCode1
Paraphrasing evades detectors of AI-generated text, but retrieval is an effective defenseCode1
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised LearningCode1
SplitOut: Out-of-the-Box Training-Hijacking Detection in Split Learning via Outlier DetectionCode1
Open-Set Likelihood Maximization for Few-Shot LearningCode1
RbA: Segmenting Unknown Regions Rejected by AllCode1
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming languageCode1
Toward Unsupervised Outlier Model SelectionCode1
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
Out-of-Distribution Detection with Hilbert-Schmidt Independence OptimizationCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
FaceMap: Towards Unsupervised Face Clustering via Map EquationCode1
NEAR - Newborns EEG Artifact RemovalCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
LUNAR: Unifying Local Outlier Detection Methods via Graph Neural NetworksCode1
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency RegularizationCode1
TOD: GPU-accelerated Outlier Detection via Tensor OperationsCode1
Generalized Out-of-Distribution Detection: A SurveyCode1
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier DetectionCode1
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
Uncertainty-Aware Reliable Text ClassificationCode1
Outlier detection in multivariate functional data through a contaminated mixture modelCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
Deep Clustering based Fair Outlier DetectionCode1
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with OutliersCode1
Autoencoding Under Normalization ConstraintsCode1
Unsupervised Offline Changepoint Detection EnsemblesCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98—Unverified
2F-t ALSTM-FCNAccuracy0.95—Unverified
3GENDISAccuracy0.94—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6—Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85—Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33—Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74—Unverified