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 326–350 of 703 papers

TitleStatusHype
Generalized Out-of-Distribution Detection: A SurveyCode1
News-based Business Sentiment and its Properties as an Economic Index—0
Large Scale Substitution-based Word Sense Induction—0
C-AllOut: Catching & Calling Outliers by Type—0
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier DetectionCode1
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset—0
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
Probabilistic Robust Autoencoders for Outlier Detection—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
A geometric perspective on functional outlier detectionCode0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Robust outlier detection by de-biasing VAE likelihoods—0
Out-of-Distribution Detection Using Outlier Detection MethodsCode0
Discovering outliers in the Mars Express thermal power consumption patterns—0
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation—0
Data-driven modeling of time-domain induced polarizationCode0
Unsupervised Outlier Detection using Memory and Contrastive Learning—0
Uncertainty-Aware Reliable Text ClassificationCode1
Meta-Learning for Relative Density-Ratio Estimation—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—0
Outlier Detection and Spatial Analysis Algorithms—0
Comparison of Outlier Detection Techniques for Structured Data—0
Out-of-Scope Intent Detection with Self-Supervision and Discriminative TrainingCode0
Towards Total Recall in Industrial Anomaly DetectionCode2
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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