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 601–650 of 703 papers

TitleStatusHype
GENDIS: GENetic DIscovery of ShapeletsCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
Generative Adversarial Active Learning for Unsupervised Outlier DetectionCode0
TGTOD: A Global Temporal Graph Transformer for Outlier Detection at ScaleCode0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
ODBO: Bayesian Optimization with Search Space Prescreening for Directed Protein EvolutionCode0
Rule-based outlier detection of AI-generated anatomy segmentationsCode0
Geometry- and Accuracy-Preserving Random Forest ProximitiesCode0
GradStop: Exploring Training Dynamics in Unsupervised Outlier Detection through GradientCode0
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative ModelsCode0
Outlier Detection in Large Radiological Datasets using UMAPCode0
Using Self-Supervised Learning Can Improve Model Robustness and UncertaintyCode0
Weighted Scaling Approach for Metabolomics Data AnalysisCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
SDCOR: Scalable Density-based Clustering for Local Outlier Detection in Massive-Scale DatasetsCode0
Anomaly Detection With Partitioning Overfitting Autoencoder EnsemblesCode0
Online Cyber-Attack Detection in Smart Grid: A Reinforcement Learning ApproachCode0
PyODDS: An End-to-End Outlier Detection SystemCode0
What Do Compressed Deep Neural Networks Forget?Code0
Classifying Idiomatic and Literal Expressions Using Topic Models and Intensity of EmotionsCode0
Can Tree Based Approaches Surpass Deep Learning in Anomaly Detection? A Benchmarking StudyCode0
Benign Samples Matter! Fine-tuning On Outlier Benign Samples Severely Breaks SafetyCode0
BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed GraphsCode0
Unsupervised Outlier Detection using Random Subspace and Subsampling Ensembles of Dirichlet Process MixturesCode0
Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble SolutionCode0
Quantile-based Maximum Likelihood Training for Outlier DetectionCode0
Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier DetectionCode0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
Valid Inference Corrected for Outlier RemovalCode0
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly DetectionCode0
Image Labels Are All You Need for Coarse Seagrass SegmentationCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Impact of Comprehensive Data Preprocessing on Predictive Modelling of COVID-19 MortalityCode0
Image Outlier Detection Without Training using RANSACCode0
Importance Sampling for Nonlinear ModelsCode0
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected ReconstructionCode0
Understanding and Mitigating the Effect of Outliers in Fair RankingCode0
The magnitude vector of imagesCode0
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++Code0
Depth-Based Object Tracking Using a Robust Gaussian FilterCode0
In search of the weirdest galaxies in the UniverseCode0
Variational Autoencoders for Anomalous Jet TaggingCode0
Integrating Network Embedding and Community Outlier Detection via Multiclass Graph DescriptionCode0
Dense open-set recognition with synthetic outliers generated by Real NVPCode0
A Knowledge Distillation Ensemble Framework for Predicting Short and Long-term Hospitalisation Outcomes from Electronic Health Records DataCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Automatic support vector data descriptionCode0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain ShiftCode0
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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