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 51–100 of 703 papers

TitleStatusHype
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
PyOD: A Python Toolbox for Scalable Outlier DetectionCode1
Probabilistic AutoencoderCode1
COPOD: Copula-Based Outlier DetectionCode1
Open-Set Likelihood Maximization for Few-Shot LearningCode1
Non-parametric online market regime detection and regime clustering for multidimensional and path-dependent data structuresCode1
NEAR - Newborns EEG Artifact RemovalCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
Autoencoding Under Normalization ConstraintsCode1
Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier DetectionCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Computationally Assisted Quality Control for Public Health Data StreamsCode1
SUOD: Toward Scalable Unsupervised Outlier DetectionCode1
Data Augmentation for Supervised Graph Outlier Detection via Latent Diffusion ModelsCode1
Deep Clustering based Fair Outlier DetectionCode1
Deep SetsCode1
Adaptive Negative Evidential Deep Learning for Open-set Semi-supervised LearningCode1
ECO-TR: Efficient Correspondences Finding Via Coarse-to-Fine RefinementCode1
Explainable Deep One-Class ClassificationCode1
Explainable outlier detection through decision tree conditioningCode1
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
An Isolation Forest Learning Based Outlier Detection Approach for Effectively Classifying Cyber Anomalies—0
A Framework for Developing and Evaluating Word Embeddings of Drug-named Entity—0
Active Relation Discovery: Towards General and Label-aware Open Relation Extraction—0
An Improved Heart Disease Prediction Using Stacked Ensemble Method—0
A New Approach To Two-View Motion Segmentation Using Global Dimension Minimization—0
A Framework for Clustering Uncertain Data—0
Component-wise Adaptive Trimming For Robust Mixture Regression—0
An Evolutionary Game based Secure Clustering Protocol with Fuzzy Trust Evaluation and Outlier Detection for Wireless Sensor Networks—0
A feature construction framework based on outlier detection and discriminative pattern mining—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
3D Scanning: A Comprehensive Survey—0
An Evaluation of Classification and Outlier Detection Algorithms—0
An Empirical Exploration of Open-Set Recognition via Lightweight Statistical Pipelines—0
Consensus Clustering: An Embedding Perspective, Extension and Beyond—0
An Efficient Outlier Detection Algorithm for Data Streaming—0
An Efficient Hashing-based Ensemble Method for Collaborative Outlier Detection—0
Active Learning of SVDD Hyperparameter Values—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
An Approximate Bayesian Long Short-Term Memory Algorithm for Outlier Detection—0
A Robust Regression Approach for Robot Model Learning—0
A Comprehensive System for Secondary Structure Analysis of Protein Models—0
3D Labeling Tool—0
A Robust Learning Algorithm for Regression Models Using Distributionally Robust Optimization under the Wasserstein Metric—0
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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