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

Showing 376400 of 4856 papers

TitleStatusHype
Fast Distance-based Anomaly Detection in Images Using an Inception-like AutoencoderCode1
FastFlow: Unsupervised Anomaly Detection and Localization via 2D Normalizing FlowsCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Beyond Individual Input for Deep Anomaly Detection on Tabular DataCode1
Diffusion Models with Implicit Guidance for Medical Anomaly DetectionCode1
Diffusion-Based Electrocardiography Noise Quantification via Anomaly DetectionCode1
An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile RobotsCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
A Comprehensive Survey on Graph Anomaly Detection with Deep LearningCode1
Does Graph Distillation See Like Vision Dataset Counterpart?Code1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
Anomaly localization by modeling perceptual featuresCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese NetworksCode1
Anomaly Heterogeneity Learning for Open-set Supervised Anomaly DetectionCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
AnomalyLLM: Few-shot Anomaly Edge Detection for Dynamic Graphs using Large Language ModelsCode1
AnomalyR1: A GRPO-based End-to-end MLLM for Industrial Anomaly DetectionCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified