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 301–350 of 4856 papers

TitleStatusHype
Crane: Context-Guided Prompt Learning and Attention Refinement for Zero-Shot Anomaly DetectionsCode1
Correlation-aware Spatial-Temporal Graph Learning for Multivariate Time-series Anomaly DetectionCode1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous DrivingCode1
GLAD: Content-aware Dynamic Graphs For Log Anomaly DetectionCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Deep Anomaly Detection with Outlier ExposureCode1
Constrained Contrastive Distribution Learning for Unsupervised Anomaly Detection and Localisation in Medical ImagesCode1
Concurrent Misclassification and Out-of-Distribution Detection for Semantic Segmentation via Energy-Based Normalizing FlowCode1
Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting DataCode1
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly DetectionCode1
Collaborative Discrepancy Optimization for Reliable Image Anomaly LocalizationCode1
Component-aware anomaly detection framework for adjustable and logical industrial visual inspectionCode1
A Shapelet-based Framework for Unsupervised Multivariate Time Series Representation LearningCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
Cheating Depth: Enhancing 3D Surface Anomaly Detection via Depth SimulationCode1
Cloze Test Helps: Effective Video Anomaly Detection via Learning to Complete Video EventsCode1
CHAD: Charlotte Anomaly DatasetCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
Change-point detection in wind turbine SCADA data for robust condition monitoring with normal behaviour modelsCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
Classification-Based Anomaly Detection for General DataCode1
Class Label-aware Graph Anomaly DetectionCode1
Clustered Hierarchical Anomaly and Outlier Detection AlgorithmsCode1
CODiT: Conformal Out-of-Distribution Detection in Time-Series DataCode1
Collaborative Learning of Anomalies with Privacy (CLAP) for Unsupervised Video Anomaly Detection: A New BaselineCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Computer Vision for Clinical Gait Analysis: A Gait Abnormality Video DatasetCode1
A Multi-Scale Decomposition MLP-Mixer for Time Series AnalysisCode1
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Coniferest: a complete active anomaly detection frameworkCode1
Contrastive Transformer-based Multiple Instance Learning for Weakly Supervised Polyp Frame DetectionCode1
DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly DetectionCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
Correlation-aware Deep Generative Model for Unsupervised Anomaly DetectionCode1
DEGAN: Time Series Anomaly Detection using Generative Adversarial Network Discriminators and Density EstimationCode1
Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly DetectionCode1
Cross-Domain Graph Anomaly Detection via Anomaly-aware Contrastive AlignmentCode1
Incomplete Multimodal Industrial Anomaly Detection via Cross-Modal DistillationCode1
Can LLMs Understand Time Series Anomalies?Code1
Current Time Series Anomaly Detection Benchmarks are Flawed and are Creating the Illusion of ProgressCode1
CutPaste: Self-Supervised Learning for Anomaly Detection and LocalizationCode1
Dynamic Addition of Noise in a Diffusion Model for Anomaly DetectionCode1
AMI-Net: Adaptive Mask Inpainting Network for Industrial Anomaly Detection and LocalizationCode1
DATE: Detecting Anomalies in Text via Self-Supervision of TransformersCode1
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security ApplicationsCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
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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