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 1351–1375 of 4856 papers

TitleStatusHype
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
Single- and Multi-Agent Private Active Sensing: A Deep Neuroevolution Approach—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
LAN: Learning Adaptive Neighbors for Real-Time Insider Threat DetectionCode1
Anomaly Detection by Adapting a pre-trained Vision Language Model—0
Detecting Anomalies in Dynamic Graphs via Memory enhanced Normality—0
Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical PerspectiveCode0
Semi-Supervised Learning for Anomaly Traffic Detection via Bidirectional Normalizing FlowsCode0
Exploiting Structural Consistency of Chest Anatomy for Unsupervised Anomaly Detection in Radiography ImagesCode1
Extracting Explanations, Justification, and Uncertainty from Black-Box Deep Neural Networks—0
Diffusion Models with Implicit Guidance for Medical Anomaly DetectionCode1
Caformer: Rethinking Time Series Analysis from Causal Perspective—0
Supervised Time Series Classification for Anomaly Detection in Subsea Engineering—0
Equipping Computational Pathology Systems with Artifact Processing Pipelines: A Showcase for Computation and Performance Trade-offsCode0
Study of the Impact of the Big Data Era on Accounting and Auditing—0
Grid Monitoring with Synchro-Waveform and AI Foundation Model Technologies—0
Toward Generalist Anomaly Detection via In-context Residual Learning with Few-shot Sample PromptsCode3
Detection of Object Throwing Behavior in Surveillance Videos—0
GlanceVAD: Exploring Glance Supervision for Label-efficient Video Anomaly DetectionCode1
Text-Guided Variational Image Generation for Industrial Anomaly Detection and Segmentation—0
RealNet: A Feature Selection Network with Realistic Synthetic Anomaly for Anomaly DetectionCode3
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
Divide and Conquer: High-Resolution Industrial Anomaly Detection via Memory Efficient Tiled EnsembleCode9
Dual-path Frequency Discriminators for Few-shot Anomaly DetectionCode1
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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