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 826–850 of 4856 papers

TitleStatusHype
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based ApproachCode0
MCDDPM: Multichannel Conditional Denoising Diffusion Model for Unsupervised Anomaly Detection in Brain MRICode1
Sparse Modelling for Feature Learning in High Dimensional Data—0
Semi-Supervised Bone Marrow Lesion Detection from Knee MRI Segmentation Using Mask Inpainting Models—0
Kinematic Detection of Anomalies in Human Trajectory DataCode0
MIMII-Gen: Generative Modeling Approach for Simulated Evaluation of Anomalous Sound Detection System—0
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Neural Collaborative Filtering to Detect Anomalies in Human Semantic TrajectoriesCode0
The Elephant in the Room: Towards A Reliable Time-Series Anomaly Detection BenchmarkCode3
Appearance Blur-driven AutoEncoder and Motion-guided Memory Module for Video Anomaly Detection—0
Machine Learning-based vs Deep Learning-based Anomaly Detection in Multivariate Time Series for Spacecraft Attitude Sensors—0
Revisiting Deep Ensemble Uncertainty for Enhanced Medical Anomaly DetectionCode1
VL4AD: Vision-Language Models Improve Pixel-wise Anomaly Detection—0
XAI-guided Insulator Anomaly Detection for Imbalanced Datasets—0
Grading and Anomaly Detection for Automated Retinal Image Analysis using Deep Learning—0
Exploring the Impact of Outlier Variability on Anomaly Detection Evaluation Metrics—0
A Multi-Level Approach for Class Imbalance Problem in Federated Learning for Remote Industry 4.0 Applications—0
VideoPatchCore: An Effective Method to Memorize Normality for Video Anomaly DetectionCode1
Leveraging Unsupervised Learning for Cost-Effective Visual Anomaly Detection—0
Anomaly Detection from a Tensor Train Perspective—0
VARADE: a Variational-based AutoRegressive model for Anomaly Detection on the EdgeCode0
MotifDisco: Motif Causal Discovery For Time Series Motifs—0
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning—0
LatentQGAN: A Hybrid QGAN with Classical Convolutional Autoencoder—0
Video-XL: Extra-Long Vision Language Model for Hour-Scale Video UnderstandingCode4
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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