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 976–1000 of 4856 papers

TitleStatusHype
Operational range bounding of spectroscopy models with anomaly detection—0
AssemAI: Interpretable Image-Based Anomaly Detection for Manufacturing PipelinesCode0
SR-CIS: Self-Reflective Incremental System with Decoupled Memory and Reasoning—0
AnomalySD: Few-Shot Multi-Class Anomaly Detection with Stable Diffusion Model—0
Individualized multi-horizon MRI trajectory prediction for Alzheimer's DiseaseCode0
Online Detection of Anomalies in Temporal Knowledge Graphs with InterpretabilityCode0
Small Object Few-shot Segmentation for Vision-based Industrial InspectionCode1
Efficient Quantum One-Class Support Vector Machines for Anomaly Detection Using Randomized Measurements and Variable Subsampling—0
Time Series Anomaly Detection with CNN for Environmental Sensors in Healthcare-IoT—0
Anomalous State Sequence Modeling to Enhance Safety in Reinforcement Learning—0
Normality Addition via Normality Detection in Industrial Image Anomaly Detection Models—0
Foundations for Unfairness in Anomaly Detection -- Case Studies in Facial Imaging Data—0
Can I trust my anomaly detection system? A case study based on explainable AICode0
Textile Anomaly Detection: Evaluation of the State-of-the-Art for Automated Quality Inspection of Carpet—0
Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection—0
Separating Novel Features for Logical Anomaly Detection: A Straightforward yet Effective Approach—0
Large Language Models for Anomaly Detection in Computational Workflows: from Supervised Fine-Tuning to In-Context LearningCode0
Global Confidence Degree Based Graph Neural Network for Financial Fraud Detection—0
Looking at Model Debiasing through the Lens of Anomaly DetectionCode0
When Text and Images Don't Mix: Bias-Correcting Language-Image Similarity Scores for Anomaly Detection—0
Hyperbolic embedding of brain networks detects regions disrupted by neurodegeneration in Alzheimer's disease—0
AdaCLIP: Adapting CLIP with Hybrid Learnable Prompts for Zero-Shot Anomaly DetectionCode3
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
Bidirectional skip-frame prediction for video anomaly detection with intra-domain disparity-driven attention—0
Towards Open-World Object-based Anomaly Detection via Self-Supervised Outlier SynthesisCode0
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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