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 126–150 of 703 papers

TitleStatusHype
HLoOP -- Hyperbolic 2-space Local Outlier Probabilities—0
Enhancing Sentiment Analysis Results through Outlier Detection Optimization—0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
Enhancing Intrusion Detection In Internet Of Vehicles Through Federated Learning—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—0
PACOL: Poisoning Attacks Against Continual Learners—0
SSB: Simple but Strong Baseline for Boosting Performance of Open-Set Semi-Supervised LearningCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support—0
FOUND: Foot Optimization with Uncertain Normals for Surface Deformation Using Synthetic DataCode1
Concept-based Anomaly Detection in Retail Stores for Automatic Correction using Mobile Robots—0
Outlier Detection Using Generative Models with Theoretical Performance Guarantees—0
MAGIC: Detecting Advanced Persistent Threats via Masked Graph Representation LearningCode1
Point Cloud Denoising and Outlier Detection with Local Geometric Structure by Dynamic Graph CNN—0
Tight Rates in Supervised Outlier Transfer Learning—0
Data Cleaning and Machine Learning: A Systematic Literature ReviewCode0
LS-VOS: Identifying Outliers in 3D Object Detections Using Latent Space Virtual Outlier Synthesis—0
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning—0
Distribution and volume based scoring for Isolation ForestsCode0
LiON: Learning Point-wise Abstaining Penalty for LiDAR Outlier DetectioN Using Diverse Synthetic DataCode1
Outlier-Insensitive Kalman Filtering: Theory and ApplicationsCode0
Boundary Peeling: Outlier Detection Method Using One-Class Peeling—0
Unsupervised Skin Lesion Segmentation via Structural Entropy Minimization on Multi-Scale Superpixel GraphsCode0
Large-scale gradient-based training of Mixtures of Factor Analyzers—0
kTrans: Knowledge-Aware Transformer for Binary Code EmbeddingCode1
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All datasetsECG5000Balance scale_class 1Breast cancer Wisconsin_class 2Breast cancer Wisconsin_class 4Fashion-MNISTGlass identificationHeart-CHepatitisInternet AdIonosphere_class bSKAB

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