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 1701–1725 of 4856 papers

TitleStatusHype
Efficient Slice Anomaly Detection Network for 3D Brain MRI Volume—0
AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection—0
Training-Free Time-Series Anomaly Detection: Leveraging Image Foundation Models—0
PoseWatch: A Transformer-based Architecture for Human-centric Video Anomaly Detection Using Spatio-temporal Pose TokenizationCode0
Channel-wise Influence: Estimating Data Influence for Multivariate Time Series—0
SPICED: Syntactical Bug and Trojan Pattern Identification in A/MS Circuits using LLM-Enhanced Detection—0
Variational autoencoder-based neural network model compression—0
Temporal Divide-and-Conquer Anomaly Actions Localization in Semi-Supervised Videos with Hierarchical Transformer—0
AnoPLe: Few-Shot Anomaly Detection via Bi-directional Prompt Learning with Only Normal SamplesCode0
SHEDAD: SNN-Enhanced District Heating Anomaly Detection for Urban Substations—0
Multi-Normal Prototypes Learning for Weakly Supervised Anomaly DetectionCode0
Hypergraph Learning based Recommender System for Anomaly Detection, Control and Optimization—0
Self-Supervised Iterative Refinement for Anomaly Detection in Industrial Quality Control—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Physics-Driven AI Correction in Laser Absorption Sensing Quantification—0
HELP: Hierarchical Embeddings-based Log Parsing—0
Rethinking Medical Anomaly Detection in Brain MRI: An Image Quality Assessment PerspectiveCode0
Distributional Drift Detection in Medical Imaging with Sketching and Fine-Tuned Transformer—0
Latent Anomaly Detection Through Density Matrices—0
Attention-Guided Perturbation for Unsupervised Image Anomaly Detection—0
Impact of Inaccurate Contamination Ratio on Robust Unsupervised Anomaly Detection—0
Unveiling the Flaws: A Critical Analysis of Initialization Effect on Time Series Anomaly Detection—0
Investigation of unsupervised and supervised hyperspectral anomaly detection—0
RW-NSGCN: A Robust Approach to Structural Attacks via Negative Sampling—0
Weakly Supervised Video Anomaly Detection and Localization with Spatio-Temporal Prompts—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