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 351–400 of 703 papers

TitleStatusHype
Anomaly Rule Detection in Sequence Data—0
Outlier Detection for Trajectories via Flow-embeddingsCode0
Isolation forests: looking beyond tree depth—0
Unsupervised Time Series Outlier Detection with Diversity-Driven Convolutional Ensembles -- Extended Version—0
Active Relation Discovery: Towards General and Label-aware OpenRE—0
Outlier Detection as Instance Selection Method for Feature Selection in Time Series Classification—0
Automatically detecting anomalous exoplanet transitsCode0
Mathematical Models for Local Sensing Hashes—0
Contextual Unsupervised Outlier Detection in Sequences—0
Real-time Wireless Transmitter Authorization: Adapting to Dynamic Authorized Sets with Information Retrieval—0
BAHP: Benchmark of Assessing Word Embeddings in Historical Portuguese—0
The magnitude vector of imagesCode0
Anomaly-Injected Deep Support Vector Data Description for Text Outlier Detection—0
News-based Business Sentiment and its Properties as an Economic Index—0
Large Scale Substitution-based Word Sense Induction—0
C-AllOut: Catching & Calling Outliers by Type—0
Distance Based Pattern Driven Mining for Outlier Detection in High Dimensional Big Dataset—0
Probabilistic Robust Autoencoders for Outlier Detection—0
When Complexity Is Good: Do We Need Recurrent Deep Learning For Time Series Outlier Detection?—0
Learning to Rank Anomalies: Scalar Performance Criteria and Maximization of Two-Sample Rank Statistics—0
A geometric perspective on functional outlier detectionCode0
Cascade Watchdog: A Multi-tiered Adversarial Guard for Outlier Detection—0
Robust outlier detection by de-biasing VAE likelihoods—0
Out-of-Distribution Detection Using Outlier Detection MethodsCode0
Discovering outliers in the Mars Express thermal power consumption patterns—0
IPOF: An Extremely and Excitingly Simple Outlier Detection Booster via Infinite Propagation—0
Data-driven modeling of time-domain induced polarizationCode0
Unsupervised Outlier Detection using Memory and Contrastive Learning—0
Meta-Learning for Relative Density-Ratio Estimation—0
Closed-Form, Provable, and Robust PCA via Leverage Statistics and Innovation Search—0
Outlier Detection and Spatial Analysis Algorithms—0
Comparison of Outlier Detection Techniques for Structured Data—0
Out-of-Scope Intent Detection with Self-Supervision and Discriminative TrainingCode0
Partial Wasserstein and Maximum Mean Discrepancy distances for bridging the gap between outlier detection and drift detectionCode0
Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment—0
Weighting vectors for machine learning: numerical harmonic analysis applied to boundary detection—0
Do We Really Need to Learn Representations from In-domain Data for Outlier Detection?—0
Towards a Model for LSH—0
Novelty Detection and Analysis of Traffic Scenario Infrastructures in the Latent Space of a Vision Transformer-Based Triplet AutoencoderCode0
ReLearn: A Robust Machine Learning Framework in Presence of Missing Data for Multimodal Stress Detection from Physiological Signals—0
Distributional Gaussian Process Layers for Outlier Detection in Image Segmentation—0
tsrobprep - an R package for robust preprocessing of time series data—0
Unsupervised Instance Selection with Low-Label, Supervised Learning for Outlier DetectionCode0
Conditional Selective Inference for Robust Regression and Outlier Detection using Piecewise-Linear Homotopy Continuation—0
Continual Learning with Fully Probabilistic Models—0
Image Modeling with Deep Convolutional Gaussian Mixture Models—0
Achieving differential privacy for k-nearest neighbors based outlier detection by data partitioning—0
Does Your Dermatology Classifier Know What It Doesn't Know? Detecting the Long-Tail of Unseen Conditions—0
A Large-scale Study on Unsupervised Outlier Model Selection: Do Internal Strategies Suffice?—0
Spatiotemporal Data Mining: A Survey on Challenges and Open Problems—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