SOTAVerified

Anomaly Detection

Anomaly Detection is a binary classification identifying unusual or unexpected patterns in a dataset, which deviate significantly from the majority of the data. The goal of anomaly detection is to identify such anomalies, which could represent errors, fraud, or other types of unusual events, and flag them for further investigation.

[Image source]: GAN-based Anomaly Detection in Imbalance Problems

Papers

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Showing 4251–4300 of 4856 papers

TitleStatusHype
CBOWRA: A Representation Learning Approach for Medication Anomaly Detection—0
WiFi Motion Detection: A Study into Efficacy and Classification—0
Detection of Shilling Attack Based on T-distribution on the Dynamic Time Intervals in Recommendation Systems—0
Hybrid Deep Network for Anomaly DetectionCode0
Anomaly Detection in Video Sequence with Appearance-Motion CorrespondenceCode0
Detecting abnormalities in resting-state dynamics: An unsupervised learning approach—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Detecting semantic anomaliesCode0
Anomaly Detection in High Dimensional DataCode0
Multi-timescale Trajectory Prediction for Abnormal Human Activity Detection—0
Deep Structured Cross-Modal Anomaly Detection—0
SpecAE: Spectral AutoEncoder for Anomaly Detection in Attributed Networks—0
Transcriptional Response of SK-N-AS Cells to Methamidophos—0
What goes around comes around: Cycle-Consistency-based Short-Term Motion Prediction for Anomaly Detection using Generative Adversarial Networks—0
Task-Oriented Optimal Sequencing of Visualization Charts—0
Abnormality Detection in Musculoskeletal Radiographs with Convolutional Neural Networks(Ensembles) and Performance Optimization—0
An Adaptive Anaphylaxis Detection and Emergency Response SystemCode0
Developing an Unsupervised Real-time Anomaly Detection Scheme for Time Series with Multi-seasonality—0
Simultaneous Semantic Segmentation and Outlier Detection in Presence of Domain ShiftCode0
TABOR: A Highly Accurate Approach to Inspecting and Restoring Trojan Backdoors in AI SystemsCode0
Learning Neural Representations for Network Anomaly DetectionCode0
Unsupervised Representation Learning and Anomaly Detection in ECG Sequences—0
MSNM-Sensor: An Applied Network Monitoring Tool for Anomaly Detection in Complex Networks and SystemsCode0
Q-MIND: Defeating Stealthy DoS Attacks in SDN with a Machine-learning based Defense Framework—0
An Encoder-Decoder Based Approach for Anomaly Detection with Application in Additive Manufacturing—0
Robust Anomaly Detection for Multivariate Time Series through Stochastic Recurrent Neural NetworkCode0
Motion-Aware Feature for Improved Video Anomaly Detection—0
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
CAMLPAD: Cybersecurity Autonomous Machine Learning Platform for Anomaly Detection—0
Spatio-Temporal Correlation Analysis of Online Monitoring Data for Anomaly Detection and Location in Distribution Networks—0
Early Anomaly Detection in Power Systems Based on Random Matrix Theory—0
Batch Uniformization for Minimizing Maximum Anomaly Score of DNN-based Anomaly Detection in Sounds—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostCode0
Exploring Deep Anomaly Detection Methods Based on Capsule NetCode0
Sequential online prediction in the presence of outliers and change points: an instant temporal structure learning approach—0
ALFA: A Dataset for UAV Fault and Anomaly DetectionCode0
Towards Robust Voice Pathology Detection—0
Automated Real-time Anomaly Detection in Human Trajectories using Sequence to Sequence Networks—0
AMAD: Adversarial Multiscale Anomaly Detection on High-Dimensional and Time-Evolving Categorical Data—0
Statistical Analysis of Nearest Neighbor Methods for Anomaly DetectionCode0
FortuneTeller: Predicting Microarchitectural Attacks via Unsupervised Deep Learning—0
Interpretable Feature Learning in Multivariate Big Data Analysis for Network Monitoring—0
Unsupervised Anomalous Trajectory Detection for Crowded Scenes—0
VELC: A New Variational AutoEncoder Based Model for Time Series Anomaly DetectionCode0
Self-Attentive, Multi-Context One-Class Classification for Unsupervised Anomaly Detection on TextCode0
Using Self-Supervised Learning Can Improve Model Robustness and UncertaintyCode0
Anomaly Subsequence Detection with Dynamic Local Density for Time Series—0
An Improvement of PAA on Trend-Based Approximation for Time Series—0
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified