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 2601–2625 of 4856 papers

TitleStatusHype
Deep Autoencoders for Anomaly Detection in Textured Images using CW-SSIMCode0
Positive Difference Distribution for Image Outlier Detection using Normalizing Flows and Contrastive Data—0
Learning Informative Health Indicators Through Unsupervised Contrastive Learning—0
RUAD: unsupervised anomaly detection in HPC systems—0
Self-Calibrating Anomaly and Change Detection for Autonomous Inspection Robots—0
Deep Learning-based ECG Classification on Raspberry PI using a Tensorflow Lite Model based on PTB-XL Dataset—0
Towards an Awareness of Time Series Anomaly Detection Models' Adversarial VulnerabilityCode0
Combining AI and AM - Improving Approximate Matching through Transformer NetworksCode0
ADMoE: Anomaly Detection with Mixture-of-Experts from Noisy Labels—0
Q-Net: Query-Informed Few-Shot Medical Image SegmentationCode1
LogLG: Weakly Supervised Log Anomaly Detection via Log-Event Graph Construction—0
Unsupervised Anomaly Localization with Structural Feature-AutoencodersCode1
Towards Open Set Video Anomaly Detection—0
An anomaly detection approach for backdoored neural networks: face recognition as a case studyCode0
Improving Multilayer-Perceptron(MLP)-based Network Anomaly Detection with Birch Clustering on CICIDS-2017 Dataset—0
Feature Selection for Fault Detection and Prediction based on Event Log Analysis—0
An Unsupervised Short- and Long-Term Mask Representation for Multivariate Time Series Anomaly DetectionCode1
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical ProcessesCode1
Evaluation of 3D GANs for Lung Tissue Modelling in Pulmonary CTCode0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
AHEAD: A Triple Attention Based Heterogeneous Graph Anomaly Detection ApproachCode0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
Unsupervised Face Morphing Attack Detection via Self-paced Anomaly DetectionCode1
Locality-aware Attention Network with Discriminative Dynamics Learning for Weakly Supervised 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