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 351–375 of 703 papers

TitleStatusHype
Outlier detection in multivariate functional data through a contaminated mixture modelCode1
InFlow: Robust outlier detection utilizing Normalizing FlowsCode1
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detectionCode0
Deep Clustering based Fair Outlier DetectionCode1
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment—0
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection—0
OpenMatch: Open-set Consistency Regularization for Semi-supervised Learning with OutliersCode1
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?—0
Autoencoding Under Normalization ConstraintsCode1
Towards a Model for LSH—0
Unsupervised Offline Changepoint Detection EnsemblesCode1
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet AutoencoderCode0
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
tsrobprep - an R package for robust preprocessing of time series data—0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Image Modeling with Deep Convolutional Gaussian Mixture Models—0
Continual Learning with Fully Probabilistic Models—0
Testing for Outliers with Conformal p-valuesCode1
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems—0
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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