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 2126–2150 of 4856 papers

TitleStatusHype
DyAnNet: A Scene Dynamicity Guided Self-Trained Video Anomaly Detection Network—0
ADPS: Asymmetric Distillation Post-Segmentation for Image Anomaly Detection—0
Dual-Student Knowledge Distillation Networks for Unsupervised Anomaly Detection—0
A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect—0
Adversarial Machine Learning Attacks Against Video Anomaly Detection Systems—0
Dual-stream spatiotemporal networks with feature sharing for monitoring animals in the home cage—0
A Survey on Unsupervised Anomaly Detection Algorithms for Industrial Images—0
Dual-Modeling Decouple Distillation for Unsupervised Anomaly Detection—0
A Survey on Time-Series Distance Measures—0
Anomaly Crossing: New Horizons for Video Anomaly Detection as Cross-domain Few-shot Learning—0
A Survey on Social Media Anomaly Detection—0
Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image Generation—0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
Anomaly Correction of Business Processes Using Transformer Autoencoder—0
Adversarially Robust Industrial Anomaly Detection Through Diffusion Model—0
Active Anomaly Detection for time-domain discoveries—0
Abnormal-aware Multi-person Evaluation System with Improved Fuzzy Weighting—0
Dual-encoder Bidirectional Generative Adversarial Networks for Anomaly Detection—0
A Survey on Proactive Customer Care: Enabling Science and Steps to Realize it—0
A Survey on Graph Representation Learning Methods—0
Dual-Branch Reconstruction Network for Industrial Anomaly Detection with RGB-D Data—0
Abnormal component analysis—0
A Survey on Explainable Anomaly Detection—0
A Survey on Embedding Dynamic Graphs—0
Dropping Activation Outputs with Localized First-layer Deep Network for Enhancing User Privacy and Data Security—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