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 151–200 of 703 papers

TitleStatusHype
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?Code0
HPSCAN: Human Perception-Based Scattered Data ClusteringCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Outlier Detection in Large Radiological Datasets using UMAPCode0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
FairOD: Fairness-aware Outlier DetectionCode0
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and TrackingCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier LogitsCode0
Anomaly Detection with Selective Dictionary LearningCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Image Labels Are All You Need for Coarse Seagrass SegmentationCode0
Condition Number Analysis of Kernel-based Density Ratio EstimationCode0
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of EmotionsCode0
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected ReconstructionCode0
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time ChangesCode0
Consistent and Flexible Selectivity Estimation for High-Dimensional DataCode0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical DataCode0
Anomaly Detection via oversampling Principal Component AnalysisCode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"Code0
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKACode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier DetectionCode0
Fluctuation-based Outlier DetectionCode0
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size ComparisonCode0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Measuring Dependence with Matrix-based Entropy FunctionalCode0
MIMIC-Extract: A Data Extraction, Preprocessing, and Representation Pipeline for MIMIC-IIICode0
MIX: A Joint Learning Framework for Detecting Both Clustered and Scattered Outliers in Mixed-Type DataCode0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Edgewise outliers of network indexed signalsCode0
Robust outlier detection by de-biasing VAE likelihoodsCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Anomaly Detection in Networks via Score-Based Generative ModelsCode0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
Efficient Subspace Search in Data StreamsCode0
Deep One-Class ClassificationCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
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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