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 226–250 of 703 papers

TitleStatusHype
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
OutlierDetection.jl: A modular outlier detection ecosystem for the Julia programming languageCode1
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets—0
Toward Unsupervised Outlier Model SelectionCode1
Meta-Learning for Unsupervised Outlier Detection with Optimal Transport—0
Learning to Detect Interesting Anomalies—0
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction—0
Unsupervised Graph Outlier Detection: Problem Revisit, New Insight, and Superior MethodCode1
Learning Universe Model for Partial Matching Networks over Multiple Graphs—0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
The Invariant Ground Truth of Affect—0
CrowdGuard: Federated Backdoor Detection in Federated LearningCode0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
Prompt-driven efficient Open-set Semi-supervised Learning—0
Out-of-Distribution Detection with Hilbert-Schmidt Independence OptimizationCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness—0
Self-Optimizing Feature Transformation—0
On Language Clustering: A Non-parametric Statistical Approach—0
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
Shaken, and Stirred: Long-Range Dependencies Enable Robust Outlier Detection with PixelCNN++Code0
Detecting Surprising Situations in Event Data—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Semantic Driven Energy based Out-of-Distribution Detection—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