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 76–100 of 703 papers

TitleStatusHype
Regularized Contrastive Partial Multi-view Outlier Detection—0
Outlier Detection in Large Radiological Datasets using UMAPCode0
STAMP: Outlier-Aware Test-Time Adaptation with Stable Memory ReplayCode1
BoBa: Boosting Backdoor Detection through Data Distribution Inference in Federated Learning—0
Rethinking Unsupervised Outlier Detection via Multiple ThresholdingCode0
SPINEX: Similarity-based Predictions with Explainable Neighbors Exploration for Anomaly and Outlier Detection—0
Machine Learning for Complex Systems with Abnormal Pattern by Exception Maximization Outlier Detection Method—0
M5 -- A Diverse Benchmark to Assess the Performance of Large Multimodal Models Across Multilingual and Multicultural Vision-Language Tasks—0
A Radiometric Correction based Optical Modeling Approach to Removing Reflection Noise in TLS Point Clouds of Urban ScenesCode0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
HAITCH: A Framework for Distortion and Motion Correction in Fetal Multi-Shell Diffusion-Weighted MRI—0
Statistical Test for Feature Selection Pipelines by Selective InferenceCode0
Evaluating the Efficacy of Foundational Models: Advancing Benchmarking Practices to Enhance Fine-Tuning Decision-Making—0
Rule-based outlier detection of AI-generated anatomy segmentationsCode0
Outlier detection in maritime environments using AIS data and deep recurrent architectures—0
Meta-learning for Positive-unlabeled Classification—0
Can Dense Connectivity Benefit Outlier Detection? An Odyssey with NAS—0
Comparative Study of Neighbor-based Methods for Local Outlier Detection—0
EntropyStop: Unsupervised Deep Outlier Detection with Loss EntropyCode0
Rethinking Graph Backdoor Attacks: A Distribution-Preserving PerspectiveCode1
A Method of Moments Embedding Constraint and its Application to Semi-Supervised LearningCode0
Fin-Fed-OD: Federated Outlier Detection on Financial Tabular Data—0
Generative Subspace Adversarial Active Learning for Outlier Detection in Multiple Views of High-dimensional DataCode0
Aggressive or Imperceptible, or Both: Network Pruning Assisted Hybrid Byzantines in Federated LearningCode0
Differential Privacy for Anomaly Detection: Analyzing the Trade-off Between Privacy and Explainability—0
Show:102550
← PrevPage 4 of 29Next →
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