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 276–300 of 703 papers

TitleStatusHype
CoMadOut -- A Robust Outlier Detection Algorithm based on CoMADCode0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets—0
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
Learning Universe Model for Partial Matching Networks over Multiple Graphs—0
D.MCA: Outlier Detection with Explicit Micro-Cluster AssignmentsCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
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
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
Outlier Detection using Self-Organizing Maps for Automated Blood Cell Analysis—0
A Hybrid Deep Feature-Based Deformable Image Registration Method for Pathology Images—0
Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud—0
SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated—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