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 1151–1175 of 4856 papers

TitleStatusHype
Beyond Conventional Transformers: The Medical X-ray Attention (MXA) Block for Improved Multi-Label Diagnosis Using Knowledge DistillationCode0
Anomaly Detection for Hybrid Butterfly Subspecies via Probability FilteringCode0
Fault injection analysis of Real NVP normalising flow model for satellite anomaly detection—0
What is AI, what is it not, how we use it in physics and how it impacts... you—0
Unleashing the Power of Pre-trained Encoders for Universal Adversarial Attack Detection—0
Detecting Localized Density Anomalies in Multivariate Data via Coin-Flip StatisticsCode0
Federated Structured Sparse PCA for Anomaly Detection in IoT Networks—0
Integrating Quantum-Classical Attention in Patch Transformers for Enhanced Time Series ForecastingCode0
Enhancing Time Series Forecasting with Fuzzy Attention-Integrated TransformersCode0
GAL-MAD: Towards Explainable Anomaly Detection in Microservice Applications Using Graph Attention Networks—0
Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly DetectionCode0
Unsupervised Anomaly Detection in Multivariate Time Series across Heterogeneous DomainsCode0
A Dataset for Semantic Segmentation in the Presence of Unknowns—0
AssistPDA: An Online Video Surveillance Assistant for Video Anomaly Prediction, Detection, and Analysis—0
iMedImage Technical Report—0
LogicQA: Logical Anomaly Detection with Vision Language Model Generated Questions—0
Refining Time Series Anomaly Detectors using Large Language Models—0
Adaptive State-Space Mamba for Real-Time Sensor Data Anomaly Detection—0
Channel impulse response peak clustering using neural networks—0
β-GNN: A Robust Ensemble Approach Against Graph Structure PerturbationCode0
Post-Hoc Calibrated Anomaly Detection—0
Social Network User Profiling for Anomaly Detection Based on Graph Neural Networks—0
CRCL: Causal Representation Consistency Learning for Anomaly Detection in Surveillance Videos—0
Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants—0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
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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