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 101–150 of 703 papers

TitleStatusHype
MaxGap Bandit: Adaptive Algorithms for Approximate RankingCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet AutoencoderCode0
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 MortalityCode0
Importance Sampling for Nonlinear ModelsCode0
A Fast Greedy Algorithm for Outlier MiningCode0
In search of the weirdest galaxies in the UniverseCode0
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence ClassificationCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Fluctuation-based Outlier DetectionCode0
FairOD: Fairness-aware Outlier DetectionCode0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban ScenesCode0
Automatic support vector data descriptionCode0
Local Subspace-Based Outlier Detection using Global NeighbourhoodsCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
Measuring Dependence with Matrix-based Entropy FunctionalCode0
Meta-survey on outlier and anomaly detectionCode0
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical DataCode0
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKACode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Multi-Person Pose Estimation with Local Joint-to-Person AssociationsCode0
Efficient variational Bayesian neural network ensembles for outlier detectionCode0
ELKI: A large open-source library for data analysis - ELKI Release 0.7.5 "Heidelberg"Code0
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time ChangesCode0
EOL: Transductive Few-Shot Open-Set Recognition by Enhancing Outlier LogitsCode0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
Robust outlier detection by de-biasing VAE likelihoodsCode0
A Novel Deep Learning Approach Featuring Graph-Based Algorithm for Cell Segmentation and TrackingCode0
ALDI++: Automatic and parameter-less discord and outlier detection for building energy load profilesCode0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
Efficient Subspace Search in Data StreamsCode0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?Code0
Outlier Detection in Large Radiological Datasets using UMAPCode0
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
Do Ensembling and Meta-Learning Improve Outlier Detection in Randomized Controlled Trials?Code0
Edgewise outliers of network indexed signalsCode0
Anomaly Detection with Selective Dictionary LearningCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of EmotionsCode0
Distribution and volume based scoring for Isolation ForestsCode0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Anomaly Detection via oversampling Principal Component 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