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 4351–4400 of 4856 papers

TitleStatusHype
Anomaly Detection With Multiple-Hypotheses PredictionsCode0
Boosting Anomaly Detection Using Unsupervised Diverse Test-Time AugmentationCode0
Detecting Abnormal Operations in Concentrated Solar Power Plants from Irregular Sequences of Thermal ImagesCode0
Task-oriented Time Series Imputation Evaluation via Generalized RepresentersCode0
A Classifier-Based Approach to Multi-Class Anomaly Detection Applied to Astronomical Time-SeriesCode0
Robustness Verification of Deep Neural Networks using Star-Based Reachability Analysis with Variable-Length Time Series InputCode0
Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep UnrollingCode0
Heartbeat Anomaly Detection using Adversarial OversamplingCode0
U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal CordCode0
Multimodal Data Fusion in High-Dimensional Heterogeneous Datasets via Generative ModelsCode0
Harnessing Collective Structure Knowledge in Data Augmentation for Graph Neural NetworksCode0
Achieving state-of-the-art performance in the Medical Out-of-Distribution (MOOD) challenge using plausible synthetic anomaliesCode0
Multimodal Industrial Anomaly Detection by Crossmodal Reverse DistillationCode0
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse EstimationCode0
Hack Me If You Can: Aggregating AutoEncoders for Countering Persistent Access Threats Within Highly Imbalanced DataCode0
Denoising Architecture for Unsupervised Anomaly Detection in Time-SeriesCode0
Anomaly Detection with Generative Adversarial Networks for Multivariate Time SeriesCode0
Defending Pre-trained Language Models from Adversarial Word Substitutions Without Performance SacrificeCode0
Blind Image Denoising and Inpainting Using Robust Hadamard AutoencodersCode0
tegdet: An extensible Python Library for Anomaly Detection using Time-Evolving GraphsCode0
Adversarially Learned Anomaly Detection on CMS Open Data: re-discovering the top quarkCode0
Robust Spectral Anomaly Detection in EELS Spectral Images via Three Dimensional Convolutional Variational AutoencodersCode0
An Autoencoder Architecture for L-band Passive Microwave Retrieval of Landscape Freeze-Thaw CycleCode0
Multiple Instance-Based Video Anomaly Detection using Deep Temporal Encoding-DecodingCode0
RobustSTL: A Robust Seasonal-Trend Decomposition Algorithm for Long Time SeriesCode0
Robust Subspace Recovery Layer for Unsupervised Anomaly DetectionCode0
Temporal anomaly detection: calibrating the surpriseCode0
Anomaly Detection with Density EstimationCode0
HACD: Harnessing Attribute Semantics and Mesoscopic Structure for Community DetectionCode0
RobustTrend: A Huber Loss with a Combined First and Second Order Difference Regularization for Time Series Trend FilteringCode0
Adversarial Distillation of Bayesian Neural Network PosteriorsCode0
Group Anomaly Detection using Deep Generative ModelsCode0
Grid HTM: Hierarchical Temporal Memory for Anomaly Detection in VideosCode0
Anomaly Detection with Adversarial Dual AutoencodersCode0
Temporal Cycle-Consistency LearningCode0
RoCA: Robust Contrastive One-class Time Series Anomaly Detection with Contaminated DataCode0
Multi-Scale One-Class Recurrent Neural Networks for Discrete Event Sequence Anomaly DetectionCode0
Anomaly Detection via Self-organizing MapCode0
Anomaly Detection via oversampling Principal Component AnalysisCode0
Data Augmentation is a Hyperparameter: Cherry-picked Self-Supervision for Unsupervised Anomaly Detection is Creating the Illusion of SuccessCode0
Graph Spatiotemporal Process for Multivariate Time Series Anomaly Detection with Missing ValuesCode0
Unsupervised Features Ranking via Coalitional Game Theory for Categorical DataCode0
Anomaly Detection via Graphical LassoCode0
DeepWalk: Online Learning of Social RepresentationsCode0
Multitask Active Learning for Graph Anomaly DetectionCode0
Deep Symmetric Autoencoders from the Eckart-Young-Schmidt PerspectiveCode0
Unsupervised Graph Anomaly Detection via Multi-Hypersphere Heterophilic Graph LearningCode0
Multitask learning for improved scour detection: A dynamic wave tank studyCode0
Unsupervised Hybrid framework for ANomaly Detection (HAND) -- applied to Screening MammogramCode0
Rule-Based Error Detection and Correction to Operationalize Movement Trajectory ClassificationCode0
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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