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 251–275 of 703 papers

TitleStatusHype
A system for exploring big data: an iterative k-means searchlight for outlier detection on open health data—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
MSS-PAE: Saving Autoencoder-based Outlier Detection from Unexpected ReconstructionCode0
GAN-RXA: A Practical Scalable Solution to Receiver-Agnostic Transmitter Fingerprinting—0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
RODD: Robust Outlier Detection in Data Cubes—0
Interpretable Outlier Summarization—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
Image Labels Are All You Need for Coarse Seagrass SegmentationCode0
Proof-of-Contribution-Based Design for Collaborative Machine Learning on Blockchain—0
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory—0
ByzSecAgg: A Byzantine-Resistant Secure Aggregation Scheme for Federated Learning Based on Coded Computing and Vector Commitment—0
Unsupervised Deep One-Class Classification with Adaptive Threshold based on Training Dynamics—0
Differentiable Outlier Detection Enable Robust Deep Multimodal AnalysisCode0
Real-Time Outlier Detection with Dynamic Process Limits—0
Conformal inference is (almost) free for neural networks trained with early stoppingCode0
ODIM: Outlier Detection via Likelihood of Under-Fitted Generative ModelsCode0
A Meta-Learning Algorithm for Interrogative Agendas—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—0
LOSDD: Leave-Out Support Vector Data Description for Outlier Detection—0
Learning Markerless Robot-Depth Camera Calibration and End-Effector Pose Estimation—0
DOC-NAD: A Hybrid Deep One-class Classifier for Network Anomaly Detection—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
FedCC: Robust Federated Learning against Model Poisoning Attacks—0
A Deep Learning Anomaly Detection Method in Textual Data—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