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
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
ast2vec: Utilizing Recursive Neural Encodings of Python ProgramsCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected ReconstructionCode0
Learning to Classify Open Intent via Soft Labeling and Manifold MixupCode0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
Fluctuation-based Outlier DetectionCode0
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
Automatically detecting anomalous exoplanet transitsCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
FairOD: Fairness-aware Outlier DetectionCode0
Automatic support vector data descriptionCode0
Measuring Dependence with Matrix-based Entropy FunctionalCode0
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban ScenesCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
GENDIS: GENetic DIscovery of ShapeletsCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Nowcasting NetworksCode0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKACode0
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
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
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 variational Bayesian neural network ensembles for outlier detectionCode0
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical DataCode0
Edgewise outliers of network indexed signalsCode0
Outlier Detection in Large Radiological Datasets using UMAPCode0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size ComparisonCode0
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
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Anomaly Detection via oversampling Principal Component AnalysisCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental 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