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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 451–500 of 4856 papers

TitleStatusHype
How To Backdoor Federated LearningCode1
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
Challenging Current Semi-Supervised Anomaly Segmentation Methods for Brain MRICode1
HRGCN: Heterogeneous Graph-level Anomaly Detection with Hierarchical Relation-augmented Graph Neural NetworksCode1
ChatGPT for Digital Forensic Investigation: The Good, The Bad, and The UnknownCode1
ICSML: Industrial Control Systems ML Framework for native inference using IEC 61131-3 codeCode1
PNI : Industrial Anomaly Detection using Position and Neighborhood InformationCode1
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced RecognitionCode1
Implicit field learning for unsupervised anomaly detection in medical imagesCode1
Improved anomaly detection by training an autoencoder with skip connections on images corrupted with Stain-shaped noiseCode1
Improving Generalizability of Graph Anomaly Detection Models via Data AugmentationCode1
Improving Position Encoding of Transformers for Multivariate Time Series ClassificationCode1
Anomaly Detection in Multi-Agent Trajectories for Automated DrivingCode1
SQUID: Deep Feature In-Painting for Unsupervised Anomaly DetectionCode1
CLIP-TSA: CLIP-Assisted Temporal Self-Attention for Weakly-Supervised Video Anomaly DetectionCode1
InsPLAD: A Dataset and Benchmark for Power Line Asset Inspection in UAV ImagesCode1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly DetectionCode1
Combining GANs and AutoEncoders for Efficient Anomaly DetectionCode1
Invariant Anomaly Detection under Distribution Shifts: A Causal PerspectiveCode1
CFA: Coupled-hypersphere-based Feature Adaptation for Target-Oriented Anomaly LocalizationCode1
CESNET-TimeSeries24: Time Series Dataset for Network Traffic Anomaly Detection and ForecastingCode1
Anomaly Detection-Based Unknown Face Presentation Attack DetectionCode1
Iterative weak/self-supervised classification framework for abnormal events detectionCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Catching Both Gray and Black Swans: Open-set Supervised Anomaly DetectionCode1
Laplacian Change Point Detection for Dynamic GraphsCode1
Anomaly Detection by Leveraging Incomplete Anomalous Knowledge with Anomaly-Aware Bidirectional GANsCode1
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Learning and Evaluating Representations for Deep One-class ClassificationCode1
Learning Decision Trees as Amortized Structure InferenceCode1
Learning Deep Feature Correspondence for Unsupervised Anomaly Detection and SegmentationCode1
Learning Graph Structures with Transformer for Multivariate Time Series Anomaly Detection in IoTCode1
Learning image representations for anomaly detection: application to discovery of histological alterations in drug developmentCode1
Learning Memory-guided Normality for Anomaly DetectionCode1
Learning Normal Dynamics in Videos with Meta Prototype NetworkCode1
ADA-GAD: Anomaly-Denoised Autoencoders for Graph Anomaly DetectionCode1
Learning Neural Set Functions Under the Optimal Subset OracleCode1
Learning to Adapt to Unseen Abnormal Activities under Weak SupervisionCode1
CHAD: Charlotte Anomaly DatasetCode1
Can Multimodal LLMs Perform Time Series Anomaly Detection?Code1
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly DetectionCode1
Camouflaged Object DetectionCode1
Locally Masked Convolution for Autoregressive ModelsCode1
Calibrated One-class Classification for Unsupervised Time Series Anomaly DetectionCode1
Can LLMs Understand Time Series Anomalies?Code1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event SequencesCode1
LogLead -- Fast and Integrated Log Loader, Enhancer, and Anomaly DetectorCode1
Challenges in Visual Anomaly Detection for Mobile RobotsCode1
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly DetectionCode1
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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