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

Showing 76100 of 703 papers

TitleStatusHype
Federated Nearest Neighbor Classification with a Colony of Fruit-Flies: With SupplementCode0
Fast Unsupervised Deep Outlier Model Selection with HypernetworksCode0
Fluctuation-based Outlier DetectionCode0
FairOD: Fairness-aware Outlier DetectionCode0
Estimating Density Models with Truncation Boundaries using Score MatchingCode0
Fast Incremental SVDD Learning Algorithm with the Gaussian KernelCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
A Fast Greedy Algorithm for Outlier MiningCode0
Enhancing Diversity in Bayesian Deep Learning via Hyperspherical Energy Minimization of CKACode0
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Adversarial Subspace Generation for Outlier Detection in High-Dimensional DataCode0
Embedding-Based Complex Feature Value Coupling Learning for Detecting Outliers in Non-IID Categorical DataCode0
Enhancing Traffic Flow Prediction using Outlier-Weighted AutoEncoders: Handling Real-Time ChangesCode0
Adversarially Learned One-Class Classifier for Novelty DetectionCode0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
Efficient Subspace Search in Data StreamsCode0
Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative NetworkCode0
MaxGap Bandit: Adaptive Algorithms for Approximate RankingCode0
Efficient Curation of Invertebrate Image Datasets Using Feature Embeddings and Automatic Size ComparisonCode0
Efficient variational Bayesian neural network ensembles for outlier detectionCode0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
Local Concept Embeddings for Analysis of Concept Distributions in Vision DNN Feature SpacesCode0
Edgewise outliers of network indexed signalsCode0
G-PECNet: Towards a Generalizable Pedestrian Trajectory Prediction SystemCode0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1VRAE+SVMAccuracy0.98Unverified
2F-t ALSTM-FCNAccuracy0.95Unverified
3GENDISAccuracy0.94Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.03Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy37.62Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy65.6Unverified
#ModelMetricClaimedVerifiedStatus
1PAEAUROC1Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy99.05Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC0.86Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.85Unverified
#ModelMetricClaimedVerifiedStatus
1MIXAUC-ROC0.93Unverified
#ModelMetricClaimedVerifiedStatus
1ASVDDAverage Accuracy86.33Unverified
#ModelMetricClaimedVerifiedStatus
1LSTMCapsAverage F10.74Unverified