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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 51–100 of 703 papers

TitleStatusHype
Computationally Assisted Quality Control for Public Health Data StreamsCode1
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in VideoCode1
Probabilistic AutoencoderCode1
RbA: Segmenting Unknown Regions Rejected by AllCode1
AdaLAM: Revisiting Handcrafted Outlier DetectionCode1
OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency RegularizationCode1
NEAR - Newborns EEG Artifact RemovalCode1
Testing for Outliers with Conformal p-valuesCode1
Learn then Test: Calibrating Predictive Algorithms to Achieve Risk ControlCode1
Automating Outlier Detection via Meta-LearningCode1
Learning Energy-Based Models in High-Dimensional Spaces with Multi-scale Denoising Score MatchingCode1
Autoencoding Under Normalization ConstraintsCode1
Coniferest: a complete active anomaly detection frameworkCode1
DEUP: Direct Epistemic Uncertainty PredictionCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
Zero-Shot Learning Through Cross-Modal TransferCode1
COPOD: Copula-Based Outlier DetectionCode1
kTrans: Knowledge-Aware Transformer for Binary Code EmbeddingCode1
Deep Clustering based Fair Outlier DetectionCode1
SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier DetectionCode1
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution FunctionsCode1
Energy-based Detection of Adverse Weather Effects in LiDAR DataCode1
Explainable Deep One-Class ClassificationCode1
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
Active Relation Discovery: Towards General and Label-aware OpenRE—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
3D Scanning: A Comprehensive Survey—0
Backdooring Outlier Detection Methods: A Novel Attack Approach—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
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—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