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 501550 of 4856 papers

TitleStatusHype
Energy TransformerCode1
Deep Orthogonal Hypersphere Compression for Anomaly DetectionCode1
Explainable Anomaly Detection in Images and Videos: A SurveyCode1
Dual Memory Units with Uncertainty Regulation for Weakly Supervised Video Anomaly DetectionCode1
Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep ModelsCode1
Weakly Supervised Anomaly Detection: A SurveyCode1
Perception Datasets for Anomaly Detection in Autonomous Driving: A SurveyCode1
Window Size Selection in Unsupervised Time Series Analytics: A Review and BenchmarkCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
Laplacian Change Point Detection for Single and Multi-view Dynamic GraphsCode1
IM-IAD: Industrial Image Anomaly Detection Benchmark in ManufacturingCode1
Exploring Image Augmentations for Siamese Representation Learning with Chest X-RaysCode1
Making Reconstruction-based Method Great Again for Video Anomaly DetectionCode1
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly DetectionCode1
Quantum anomaly detection in the latent space of proton collision events at the LHCCode1
Hybrid Open-set Segmentation with Synthetic Negative DataCode1
The role of noise in denoising models for anomaly detection in medical imagesCode1
Subgraph Centralization: A Necessary Step for Graph Anomaly DetectionCode1
FewSOME: One-Class Few Shot Anomaly Detection with Siamese NetworksCode1
On Advantages of Mask-level Recognition for Outlier-aware SegmentationCode1
Self-Supervised Video Forensics by Audio-Visual Anomaly DetectionCode1
Unsupervised Multivariate Time-Series Transformers for Seizure Identification on EEGCode1
Revisiting Reverse Distillation for Anomaly DetectionCode1
Remembering Normality: Memory-guided Knowledge Distillation for Unsupervised Anomaly DetectionCode1
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class ClassificationCode1
Anomaly Detection using Score-based Perturbation ResilienceCode1
Federated PCA on Grassmann Manifold for Anomaly Detection in IoT NetworksCode1
SiteFerret: beyond simple pocket identification in proteinsCode1
CHAD: Charlotte Anomaly DatasetCode1
FedTADBench: Federated Time-Series Anomaly Detection BenchmarkCode1
Lorentz group equivariant autoencodersCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
On Root Cause Localization and Anomaly Mitigation through Causal InferenceCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
An Attribute-based Method for Video Anomaly DetectionCode1
MGFN: Magnitude-Contrastive Glance-and-Focus Network for Weakly-Supervised Video Anomaly DetectionCode1
Residual Pattern Learning for Pixel-wise Out-of-Distribution Detection in Semantic SegmentationCode1
MAEDAY: MAE for few and zero shot AnomalY-DetectionCode1
RbA: Segmenting Unknown Regions Rejected by AllCode1
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised ThresholdCode1
DeSTSeg: Segmentation Guided Denoising Student-Teacher for Anomaly DetectionCode1
Normalizing Flows for Human Pose Anomaly DetectionCode1
Are we certain it's anomalous?Code1
Anomaly Detection in Multiplex Dynamic Networks: from Blockchain Security to Brain Disease PredictionCode1
FAPM: Fast Adaptive Patch Memory for Real-time Industrial Anomaly DetectionCode1
LGN-Net: Local-Global Normality Network for Video Anomaly DetectionCode1
Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution DetectionCode1
Deep Learning for Time Series Anomaly Detection: A SurveyCode1
A Comprehensive Survey of Regression Based Loss Functions for Time Series ForecastingCode1
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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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (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