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 1951–1975 of 4856 papers

TitleStatusHype
CSCAD: Correlation Structure-based Collective Anomaly Detection in Complex System—0
Few-shot Anomaly Detection in Text with Deviation Learning—0
An Adaptive Event-based Data Converter for Always-on Biomedical Applications at the Edge—0
GLOSS: Tensor-Based Anomaly Detection in Spatiotemporal Urban Traffic Data—0
Anomaly Detection for High-Dimensional Data Using Large Deviations Principle—0
Excision And Recovery: Visual Defect Obfuscation Based Self-Supervised Anomaly Detection Strategy—0
Automatic Interaction and Activity Recognition from Videos of Human Manual Demonstrations with Application to Anomaly Detection—0
GODS: Generalized One-class Discriminative Subspaces for Anomaly Detection—0
Deep Representation Learning with an Information-theoretic Loss—0
Experimental Assessment of Neural 3D Reconstruction for Small UAV-based Applications—0
Automatic Prompt Generation and Grounding Object Detection for Zero-Shot Image Anomaly Detection—0
Experiments on Anomaly Detection in Autonomous Driving by Forward-Backward Style Transfers—0
Expert enhanced dynamic time warping based anomaly detection—0
Explainable AI (XAI) for PHM of Industrial Asset: A State-of-The-Art, PRISMA-Compliant Systematic Review—0
Crowd Scene Analysis using Deep Learning Techniques—0
Explainable Anomaly Detection: Counterfactual driven What-If Analysis—0
Crowd-level Abnormal Behavior Detection via Multi-scale Motion Consistency Learning—0
An Adaptive Approach for Anomaly Detector Selection and Fine-Tuning in Time Series—0
Crowded Scene Analysis: A Survey—0
Anomaly Detection with Prototype-Guided Discriminative Latent Embeddings—0
Anomaly Detection for Industrial Big Data—0
Explainable Machine Learning-Based Security and Privacy Protection Framework for Internet of Medical Things Systems—0
A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency—0
Explainable multi-class anomaly detection on functional data—0
Fence Theorem: Preprocessing is Dual-Objective Semantic Structure Isolator in 3D Anomaly Detection—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