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 526–550 of 703 papers

TitleStatusHype
Oaken: Fast and Efficient LLM Serving with Online-Offline Hybrid KV Cache Quantization—0
Ocean Data Quality Assessment through Outlier Detection-enhanced Active Learning—0
ODDR: Outlier Detection & Dimension Reduction Based Defense Against Adversarial Patches—0
One-Class Kernel Spectral Regression—0
One-Class SVM on siamese neural network latent space for Unsupervised Anomaly Detection on brain MRI White Matter Hyperintensities—0
On Integrated Clustering and Outlier Detection—0
Onion-Peeling Outlier Detection in 2-D data Sets—0
On Language Clustering: A Non-parametric Statistical Approach—0
On Soft Power Diagrams—0
On the Adversarial Robustness of Benjamini Hochberg—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
Optimization of Retrieval-Augmented Generation Context with Outlier Detection—0
Ordinal time series analysis with the R package otsfeatures—0
OuroMamba: A Data-Free Quantization Framework for Vision Mamba Models—0
OutCenTR: A novel semi-supervised framework for predicting exploits of vulnerabilities in high-dimensional datasets—0
Outlier-based Autism Detection using Longitudinal Structural MRI—0
Outlier Cluster Formation in Spectral Clustering—0
Outlier Detection and Data Clustering via Innovation Search—0
Outlier Detection and Robust PCA Using a Convex Measure of Innovation—0
Outlier Detection and Spatial Analysis Algorithms—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Efficient Generation of Hidden Outliers for Improved Outlier DetectionCode0
ast2vec: Utilizing Recursive Neural Encodings of Python ProgramsCode0
Out-of-Scope Intent Detection with Self-Supervision and Discriminative TrainingCode0
Efficient Subspace Search in Data StreamsCode0
Show:102550
← PrevPage 22 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