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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 2176–2200 of 4856 papers

TitleStatusHype
Unsupversied feature correlation model to predict breast abnormal variation maps in longitudinal mammogramsCode0
Anticipated Network Surveillance -- An extrapolated study to predict cyber-attacks using Machine Learning and Data Analytics—0
ReSynthDetect: A Fundus Anomaly Detection Network with Reconstruction and Synthetic Features—0
Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection—0
Abnormal component analysis—0
Are you sure it’s an artifact? Artifact detection and uncertainty quantification in histological imagesCode0
Understanding normalization in contrastive representation learning and out-of-distribution detectionCode0
Generative Pretraining at Scale: Transformer-Based Encoding of Transactional Behavior for Fraud Detection—0
Progressing from Anomaly Detection to Automated Log Labeling and Pioneering Root Cause Analysis—0
Self-supervised Complex Network for Machine Sound Anomaly Detection—0
Comparative Evaluation of Anomaly Detection Methods for Fraud Detection in Online Credit Card Payments—0
Fast kernel half-space depth for data with non-convex supports—0
Evolutionary Optimization of 1D-CNN for Non-contact Respiration Pattern Classification—0
Produce Once, Utilize Twice for Anomaly Detection—0
Machine Learning for Anomaly Detection in Particle Physics—0
CRD: Collaborative Representation Distance for Practical Anomaly Detection—0
PARs: Predicate-based Association Rules for Efficient and Accurate Model-Agnostic Anomaly ExplanationCode0
A Survey on IoT Ground Sensing Systems for Early Wildfire Detection: Technologies, Challenges and Opportunities—0
Residual ANODE—0
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks—0
Towards Efficient Quantum Anomaly Detection: One-Class SVMs using Variable Subsampling and Randomized MeasurementsCode0
Guarding the Grid: Enhancing Resilience in Automated Residential Demand Response Against False Data Injection Attacks—0
Deep Anomaly Detection in Text—0
Efficient Representation of the Activation Space in Deep Neural Networks—0
Exploring Plain ViT Reconstruction for Multi-class Unsupervised 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