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 3651–3675 of 4856 papers

TitleStatusHype
HRN: A Holistic Approach to One Class LearningCode1
Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors—0
Time Series Change Point Detection with Self-Supervised Contrastive Predictive CodingCode1
Unsupervised anomaly segmentation via deep feature reconstructionCode1
Fast and Accurate Anomaly Detection in Dynamic Graphs with a Two-Pronged ApproachCode1
Simple statistical methods for unsupervised brain anomaly detection on MRI are competitive to deep learning methodsCode0
Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
PS-DeVCEM: Pathology-sensitive deep learning model for video capsule endoscopy based on weakly labeled data—0
Self-Supervised Transformers for Activity Classification using Ambient Sensors—0
Multiresolution Knowledge Distillation for Anomaly DetectionCode1
Efficient Consensus Model based on Proximal Gradient Method applied to Convolutional Sparse Problems—0
Preparing Weather Data for Real-Time Building Energy Simulation—0
Sub-clusters of Normal Data for Anomaly Detection—0
PaDiM: a Patch Distribution Modeling Framework for Anomaly Detection and LocalizationCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Learning normal appearance for fetal anomaly screening: Application to the unsupervised detection of Hypoplastic Left Heart Syndrome—0
Anomaly Detection in Video via Self-Supervised and Multi-Task LearningCode1
Dependency-based Anomaly Detection: a General Framework and Comprehensive Evaluation—0
Local Anomaly Detection in Videos using Object-Centric Adversarial Learning—0
Real-World Anomaly Detection by using Digital Twin Systems and Weakly-Supervised Learning—0
Image Anomaly Detection by Aggregating Deep Pyramidal Representations—0
A Transfer Learning Framework for Anomaly Detection Using Model of Normality—0
Testing for Typicality with Respect to an Ensemble of Learned Distributions—0
Statistical learning for change point and anomaly detection in graphs—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