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 626–650 of 4856 papers

TitleStatusHype
CLIP-FSAC++: Few-Shot Anomaly Classification with Anomaly Descriptor Based on CLIPCode0
State Frequency Estimation for Anomaly Detection—0
UniVAD: A Training-free Unified Model for Few-shot Visual Anomaly Detection—0
Frequency-Guided Diffusion Model with Perturbation Training for Skeleton-Based Video Anomaly DetectionCode1
Sifting through the haystack -- efficiently finding rare animal behaviors in large-scale datasetsCode0
Deep Learning, Machine Learning, Advancing Big Data Analytics and Management—0
An Automated Data Mining Framework Using Autoencoders for Feature Extraction and Dimensionality Reduction—0
F-SE-LSTM: A Time Series Anomaly Detection Method with Frequency Domain InformationCode1
Representation Learning for Time-Domain High-Energy Astrophysics: Discovery of Extragalactic Fast X-ray Transient XRT 200515Code0
VERA: Explainable Video Anomaly Detection via Verbalized Learning of Vision-Language Models—0
Exploring Large Vision-Language Models for Robust and Efficient Industrial Anomaly Detection—0
Deep evolving semi-supervised anomaly detection—0
Vision Technologies with Applications in Traffic Surveillance Systems: A Holistic Survey—0
Friend or Foe? Harnessing Controllable Overfitting for Anomaly Detection—0
Real-Time Anomaly Detection in Video Streams—0
Real-time Anomaly Detection at the L1 Trigger of CMS Experiment—0
Unsupervised Learning Approach to Anomaly Detection in Gravitational Wave Data—0
Enhanced anomaly detection in well log data through the application of ensemble GANsCode0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Rock the KASBA: Blazingly Fast and Accurate Time Series Clustering—0
GraphSubDetector: Time Series Subsequence Anomaly Detection via Density-Aware Adaptive Graph Neural Network—0
A Machine Learning-based Anomaly Detection Framework in Life Insurance Contracts—0
Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
FUN-AD: Fully Unsupervised Learning for Anomaly Detection with Noisy Training DataCode1
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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