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 1601–1625 of 4856 papers

TitleStatusHype
rECGnition_v1.0: Arrhythmia detection using cardiologist-inspired multi-modal architecture incorporating demographic attributes in ECG—0
On The Relationship between Visual Anomaly-free and Anomalous Representations—0
Extreme Value Modelling of Feature Residuals for Anomaly Detection in Dynamic Graphs—0
MTFL: Multi-Timescale Feature Learning for Weakly-Supervised Anomaly Detection in Surveillance VideosCode0
Applying Quantum Autoencoders for Time Series Anomaly Detection—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
BlockFound: Customized blockchain foundation model for anomaly detection—0
Beyond Forecasting: Compositional Time Series Reasoning for End-to-End Task Execution—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
Uncertainty-aware Human Mobility Modeling and Anomaly Detection—0
HyperBrain: Anomaly Detection for Temporal Hypergraph Brain NetworksCode0
RADAR: Robust Two-stage Modality-incomplete Industrial Anomaly Detection—0
Incorporating Metabolic Information into LLMs for Anomaly Detection in Clinical Time-Series—0
LEGO: Learnable Expansion of Graph Operators for Multi-Modal Feature Fusion—0
Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework—0
Interactive Explainable Anomaly Detection for Industrial Settings—0
AI Persuasion, Bayesian Attribution, and Career Concerns of Doctors—0
Novel machine learning applications at the LHC—0
Multi-Scale Convolutional LSTM with Transfer Learning for Anomaly Detection in Cellular Networks—0
What Information Contributes to Log-based Anomaly Detection? Insights from a Configurable Transformer-Based ApproachCode0
Constraining Anomaly Detection with Anomaly-Free Regions—0
VMAD: Visual-enhanced Multimodal Large Language Model for Zero-Shot Anomaly Detection—0
Sparse Modelling for Feature Learning in High Dimensional Data—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
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
7DDADDetection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9PBASDetection AUROC99.8—Unverified
10HETMMDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4DDADDetection AUROC98.9—Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
6INP-Former ViT-B (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