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 226–250 of 703 papers

TitleStatusHype
Elastic Similarity and Distance Measures for Multivariate Time SeriesCode0
Dimensionality-Aware Outlier Detection: Theoretical and Experimental AnalysisCode0
Automated Generation of Multilingual Clusters for the Evaluation of Distributed RepresentationsCode0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
Intrinsic Dimensionality Estimation within Tight Localities: A Theoretical and Experimental AnalysisCode0
Unleashing the Potential of Unsupervised Deep Outlier Detection through Automated Training StoppingCode0
Benchmarking Unsupervised Outlier Detection with Realistic Synthetic Data—0
AnoMalNet: Outlier Detection based Malaria Cell Image Classification Method Leveraging Deep Autoencoder—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
Efficient Bregman Range Search—0
Backdooring Outlier Detection Methods: A Novel Attack Approach—0
A General Framework for Density Based Time Series Clustering Exploiting a Novel Admissible Pruning Strategy—0
Backdoor Defense in Federated Learning Using Differential Testing and Outlier Detection—0
ECORS: An Ensembled Clustering Approach to Eradicate The Local And Global Outlier In Collaborative Filtering Recommender System—0
AWT -- Clustering Meteorological Time Series Using an Aggregated Wavelet Tree—0
Annealed Denoising score matching: learning Energy based model in high-dimensional spaces—0
ECO-AMLP: A Decision Support System using an Enhanced Class Outlier with Automatic Multilayer Perceptron for Diabetes Prediction—0
AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning—0
DRGRADUATE: uncertainty-aware deep learning-based diabetic retinopathy grading in eye fundus images—0
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?—0
EDoG: Adversarial Edge Detection For Graph Neural Networks—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
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
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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