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 576–600 of 703 papers

TitleStatusHype
FairOD: Fairness-aware Outlier DetectionCode0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
An Overview and a Benchmark of Active Learning for Outlier Detection with One-Class ClassifiersCode0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Probing Predictions on OOD Images via Nearest CategoriesCode0
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
Robust Variational Autoencoders for Outlier Detection and Repair of Mixed-Type DataCode0
Unsupervised Keyphrase Extraction from Scientific PublicationsCode0
RODEO: Robust Outlier Detection via Exposing Adaptive Out-of-Distribution SamplesCode0
Anomaly Detection with Selective Dictionary LearningCode0
Non-Negative Bregman Divergence Minimization for Deep Direct Density Ratio EstimationCode0
A Probabilistic Transformation of Distance-Based OutliersCode0
CleanSurvival: Automated data preprocessing for time-to-event models using reinforcement learningCode0
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet AutoencoderCode0
Fluctuation-based Outlier DetectionCode0
Diversify and Conquer: Open-set Disagreement for Robust Semi-supervised Learning with OutliersCode0
Further Analysis of Outlier Detection with Deep Generative ModelsCode0
Distribution and volume based scoring for Isolation ForestsCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
Nowcasting NetworksCode0
Testing and Improving the Robustness of Amortized Bayesian Inference for Cognitive ModelsCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
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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