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 2351–2400 of 4856 papers

TitleStatusHype
Detecting Gait Abnormalities in Foot-Floor Contacts During Walking Through Footstep-Induced Structural Vibrations—0
Are pre-trained CNNs good feature extractors for anomaly detection in surveillance videos?—0
An Explainable Anomaly Detection Framework for Monitoring Depression and Anxiety Using Consumer Wearable Devices—0
Advancing Video Anomaly Detection: A Concise Review and a New Dataset—0
A Contrario multi-scale anomaly detection method for industrial quality inspection—0
ABBA: Adaptive Brownian bridge-based symbolic aggregation of time series—0
Visual Anomaly Detection under Complex View-Illumination Interplay: A Large-Scale Benchmark—0
Detecting Financial Market Manipulation with Statistical Physics Tools—0
Detecting Faults during Automatic Screwdriving: A Dataset and Use Case of Anomaly Detection for Automatic Screwdriving—0
A Representation Learning Approach to Feature Drift Detection in Wireless Networks—0
Detecting fake accounts through Generative Adversarial Network in online social media—0
EVBattery: A Large-Scale Electric Vehicle Dataset for Battery Health and Capacity Estimation—0
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
An Expert Ensemble for Detecting Anomalous Scenes, Interactions, and Behaviors in Autonomous Driving—0
Detecting Driver Drowsiness as an Anomaly Using LSTM Autoencoders—0
Detecting Disengagement in Virtual Learning as an Anomaly using Temporal Convolutional Network Autoencoder—0
Are Large Language Models Useful for Time Series Data Analysis?—0
Detecting Cyberattacks in Industrial Control Systems Using Convolutional Neural Networks—0
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noise—0
A new Video Synopsis Based Approach Using Stereo Camera—0
Advances on the classification of radio image cubes—0
Detecting Contextual Network Anomalies with Graph Neural Networks—0
Detecting Contextual Anomalies by Discovering Consistent Spatial Regions—0
A Recover-then-Discriminate Framework for Robust Anomaly Detection—0
Detecting Compromised IoT Devices Using Autoencoders with Sequential Hypothesis Testing—0
Detecting Clusters of Anomalies on Low-Dimensional Feature Subsets with Application to Network Traffic Flow Data—0
A Reconfigurable Low Power High Throughput Architecture for Deep Network Training—0
A New Time Series Similarity Measure and Its Smart Grid Applications—0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection—0
Detecting Attacks on IoT Devices using Featureless 1D-CNN—0
Are Anomaly Scores Telling the Whole Story? A Benchmark for Multilevel Anomaly Detection—0
Detecting Anomaly in Chemical Sensors via L1-Kernels based Principal Component Analysis—0
Detecting Anomalous User Behavior in Remote Patient Monitoring—0
A Real-time Anomaly Detection Using Convolutional Autoencoder with Dynamic Threshold—0
A New Perspective on Time Series Anomaly Detection: Faster Patch-based Broad Learning System—0
A Dual-Path Framework with Frequency-and-Time Excited Network for Anomalous Sound Detection—0
A Continual and Incremental Learning Approach for TinyML On-device Training Using Dataset Distillation and Model Size Adaption—0
Detecting Anomalous Invoice Line Items in the Legal Case Lifecycle—0
Detecting Anomalous Cryptocurrency Transactions: an AML/CFT Application of Machine Learning-based Forensics—0
Architecture of Data Anomaly Detection-Enhanced Decentralized Expert System for Early-Stage Alzheimer's Disease Prediction—0
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19—0
Detecting Anomalies Using Rotated Isolation Forest—0
Detecting Anomalies using Generative Adversarial Networks on Images—0
Detecting Anomalies Through Contrast in Heterogeneous Data—0
A new interpretable unsupervised anomaly detection method based on residual explanation—0
ADTR: Anomaly Detection Transformer with Feature Reconstruction—0
Detecting Anomalies in Blockchain Transactions using Machine Learning Classifiers and Explainability Analysis—0
ARCADE: Adversarially Regularized Convolutional Autoencoder for Network Anomaly Detection—0
Detecting Anomalies from Video-Sequences: a Novel Descriptor—0
Arbitrary Discrete Sequence Anomaly Detection with Zero Boundary LSTM—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