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 801–825 of 4856 papers

TitleStatusHype
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
Can LLMs Understand Time Series Anomalies?Code1
Neural Fourier Modelling: A Highly Compact Approach to Time-Series AnalysisCode1
BlockFound: Customized blockchain foundation model for anomaly detection—0
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Selective Test-Time Adaptation for Unsupervised Anomaly Detection using Neural Implicit RepresentationsCode0
Did You Hear That? Introducing AADG: A Framework for Generating Benchmark Data in Audio Anomaly Detection—0
Domain-Specific Retrieval-Augmented Generation Using Vector Stores, Knowledge Graphs, and Tensor Factorization—0
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series—0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
Uncertainty-aware Human Mobility Modeling and Anomaly Detection—0
LEGO: Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion—0
RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection—0
Interactive Explainable Anomaly Detection for Industrial Settings—0
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors—0
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework—0
PointAD: Comprehending 3D Anomalies from Points and Pixels for Zero-shot 3D Anomaly DetectionCode2
RAD: A Dataset and Benchmark for Real-Life Anomaly Detection with Robotic ObservationsCode1
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks—0
CableInspect-AD: An Expert-Annotated Anomaly Detection DatasetCode1
Novel machine learning applications at the LHC—0
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection—0
Constraining Anomaly Detection with Anomaly-Free Regions—0
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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