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

Showing 45014525 of 4856 papers

TitleStatusHype
Null Hypothesis Test for Anomaly DetectionCode0
Understanding the Effect of Bias in Deep Anomaly DetectionCode0
From Zero to Hero: Cold-Start Anomaly DetectionCode0
The role of data embedding in quantum autoencoders for improved anomaly detectionCode0
Self-Supervised Anomaly Detection by Self-Distillation and Negative SamplingCode0
Benchmarking Unsupervised Strategies for Anomaly Detection in Multivariate Time SeriesCode0
Object-centric Auto-encoders and Dummy Anomalies for Abnormal Event Detection in VideoCode0
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
DeepHYDRA: Resource-Efficient Time-Series Anomaly Detection in Dynamically-Configured SystemsCode0
The Semantic Knowledge Graph: A compact, auto-generated model for real-time traversal and ranking of any relationship within a domainCode0
OCGEC: One-class Graph Embedding Classification for DNN Backdoor DetectionCode0
From Vision to Sound: Advancing Audio Anomaly Detection with Vision-Based AlgorithmsCode0
DeepGrav: Anomalous Gravitational-Wave Detection Through Deep Latent FeaturesCode0
ODDObjects: A Framework for Multiclass Unsupervised Anomaly Detection on Masked ObjectsCode0
From Chaos to Clarity: Time Series Anomaly Detection in Astronomical ObservationsCode0
An Adaptive Anaphylaxis Detection and Emergency Response SystemCode0
The Tradeoff Between Privacy and Accuracy in Anomaly Detection Using Federated XGBoostCode0
OIL-AD: An Anomaly Detection Framework for Sequential Decision SequencesCode0
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator InterestCode0
FractalAD: A simple industrial anomaly detection method using fractal anomaly generation and backbone knowledge distillationCode0
OLED: One-Class Learned Encoder-Decoder Network with Adversarial Context Masking for Novelty DetectionCode0
Foundation Models for Structural Health MonitoringCode0
OML-AD: Online Machine Learning for Anomaly Detection in Time Series DataCode0
Benchmarking Suite for Synthetic Aperture Radar Imagery Anomaly Detection (SARIAD) AlgorithmsCode0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite ImagesCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6HETMMDetection AUROC99.8Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9DDADDetection AUROC99.8Unverified
10PBASDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
5DDADDetection AUROC98.9Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified