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 426–450 of 703 papers

TitleStatusHype
That's BAD: Blind Anomaly Detection by Implicit Local Feature Clustering—0
The Clever Hans Effect in Anomaly Detection—0
The Familiarity Hypothesis: Explaining the Behavior of Deep Open Set Methods—0
The Geometry of Nodal Sets and Outlier Detection—0
The ILSP/ARC submission to the WMT 2018 Parallel Corpus Filtering Shared Task—0
ODBAE: a high-performance model identifying complex phenotypes in high-dimensional biological datasets—0
The Invariant Ground Truth of Affect—0
The JHU Parallel Corpus Filtering Systems for WMT 2018—0
Tight Rates in Supervised Outlier Transfer Learning—0
Towards a Model for LSH—0
Towards an Ensemble Regressor Model for Anomalous ISP Traffic Prediction—0
Towards Auditing Unsupervised Learning Algorithms and Human Processes For Fairness—0
Towards Building Affect sensitive Word Distributions—0
Towards Reliable Zero Shot Classification in Self-Supervised Models with Conformal Prediction—0
Hyperparameter Optimization for Unsupervised Outlier Detection—0
Traffic congestion anomaly detection and prediction using deep learning—0
Transfer Neyman-Pearson Algorithm for Outlier Detection—0
Transformation Autoregressive Networks—0
TRIDIS: A Comprehensive Medieval and Early Modern Corpus for HTR and NER—0
Trojan Attacks on Wireless Signal Classification with Adversarial Machine Learning—0
tsrobprep - an R package for robust preprocessing of time series data—0
Two-phase Dual COPOD Method for Anomaly Detection in Industrial Control System—0
Uncertainty in Supply Chain Digital Twins: A Quantum-Classical Hybrid Approach—0
Understanding the Structure of QM7b and QM9 Quantum Mechanical Datasets Using Unsupervised Learning—0
Unified Graph based Multi-Cue Feature Fusion for Robust Visual Tracking—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