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 2051–2075 of 4856 papers

TitleStatusHype
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical PerspectiveCode0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks—0
Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing FlowsCode0
Caformer: Rethinking Time Series Analysis from Causal Perspective—0
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offsCode0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Study of the Impact of the Big Data Era on Accounting and Auditing—0
Detection of Object Throwing Behavior in Surveillance Videos—0
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies—0
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation—0
Learning Expressive And Generalizable Motion Features For Face Forgery Detection—0
Simulating Battery-Powered TinyML Systems Optimised using Reinforcement Learning in Image-Based Anomaly Detection—0
Exploring the Influence of Dimensionality Reduction on Anomaly Detection Performance in Multivariate Time SeriesCode0
Effectiveness Assessment of Recent Large Vision-Language Models—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Signature Isolation Forest—0
Multimodal Anomaly Detection based on Deep Auto-Encoder for Object Slip Perception of Mobile Manipulation Robots—0
Portraying the Need for Temporal Data in Flood Detection via Sentinel-1—0
Three Revisits to Node-Level Graph Anomaly Detection: Outliers, Message Passing and Hyperbolic Neural NetworksCode0
Interactive Bayesian Generative Models for Abnormality Detection in Vehicular Networks—0
Enhancing Security in Federated Learning through Adaptive Consensus-Based Model Update Validation—0
Unsupervised Distance Metric Learning for Anomaly Detection Over Multivariate Time Series—0
Towards efficient deep autoencoders for multivariate time series anomaly detection—0
CSE: Surface Anomaly Detection with Contrastively Selected EmbeddingCode0
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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