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 851900 of 4856 papers

TitleStatusHype
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
Self-supervised Learning of Echocardiographic Video Representations via Online Cluster DistillationCode1
Self-Supervised Likelihood Estimation with Energy Guidance for Anomaly Segmentation in Urban ScenesCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Self-Supervised Spatial-Temporal Normality Learning for Time Series Anomaly DetectionCode1
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly SegmentationCode1
Semi-supervised Anomaly Detection using AutoEncodersCode1
SensorSCAN: Self-Supervised Learning and Deep Clustering for Fault Diagnosis in Chemical ProcessesCode1
Sequential keypoint density estimator: an overlooked baseline of skeleton-based video anomaly detectionCode1
Set Features for Fine-grained Anomaly DetectionCode1
Shape-Guided: Shape-Guided Dual-Memory Learning for 3D Anomaly DetectionCode1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
SLADE: Detecting Dynamic Anomalies in Edge Streams without Labels via Self-Supervised LearningCode1
A Survey of Visual Sensory Anomaly DetectionCode1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
Small Object Few-shot Segmentation for Vision-based Industrial InspectionCode1
Exathlon: A Benchmark for Explainable Anomaly Detection over Time SeriesCode1
Camouflaged Object DetectionCode1
Can LLMs Understand Time Series Anomalies?Code1
CHAD: Charlotte Anomaly DatasetCode1
C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic ForecastingCode1
Building an Automated and Self-Aware Anomaly Detection SystemCode1
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
Broiler-Net: A Deep Convolutional Framework for Broiler Behavior Analysis in Poultry HousesCode1
Spatial-aware Attention Generative Adversarial Network for Semi-supervised Anomaly Detection in Medical ImageCode1
SpectrumFM: A Foundation Model for Intelligent Spectrum ManagementCode1
Brainomaly: Unsupervised Neurologic Disease Detection Utilizing Unannotated T1-weighted Brain MR ImagesCode1
BEAS: Blockchain Enabled Asynchronous & Secure Federated Machine LearningCode1
BSDM: Background Suppression Diffusion Model for Hyperspectral Anomaly DetectionCode1
StRegA: Unsupervised Anomaly Detection in Brain MRIs using a Compact Context-encoding Variational AutoencoderCode1
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networksCode1
Subgraph Centralization: A Necessary Step for Graph Anomaly DetectionCode1
Asymmetric Student-Teacher Networks for Industrial Anomaly DetectionCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
Active Anomaly Detection via EnsemblesCode1
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
CwA-T: A Channelwise AutoEncoder with Transformer for EEG Abnormality DetectionCode1
Boosting Fine-Grained Visual Anomaly Detection with Coarse-Knowledge-Aware Adversarial LearningCode1
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
Target before Shooting: Accurate Anomaly Detection and Localization under One Millisecond via Cascade Patch RetrievalCode1
TeD-SPAD: Temporal Distinctiveness for Self-supervised Privacy-preservation for video Anomaly DetectionCode1
BMAD: Benchmarks for Medical Anomaly DetectionCode1
Anomaly Detection in Aerial Videos with TransformersCode1
Blind Localization and Clustering of Anomalies in TexturesCode1
The OPS-SAT benchmark for detecting anomalies in satellite telemetryCode1
The role of noise in denoising models for anomaly detection in medical imagesCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
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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