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 39513975 of 4856 papers

TitleStatusHype
Semi-supervised Deep Embedded Clustering with Anomaly Detection for Semantic Frame InductionCode0
Treating Dialogue Quality Evaluation as an Anomaly Detection Problem0
Learning Compliance Adaptation in Contact-Rich Manipulation0
The 4th AI City Challenge0
Data-Driven Construction of Data Center Graph of Things for Anomaly Detection0
Real-Time Anomaly Detection in Data Centers for Log-based Predictive Maintenance using an Evolving Fuzzy-Rule-Based Approach0
A Survey on Incorporating Domain Knowledge into Deep Learning for Medical Image Analysis0
Local Adaptation Improves Accuracy of Deep Learning Model for Automated X-Ray Thoracic Disease Detection : A Thai Study0
How to find a unicorn: a novel model-free, unsupervised anomaly detection method for time seriesCode1
Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing0
A Kernel Two-sample Test for Dynamical Systems0
Discovering Imperfectly Observable Adversarial Actions using Anomaly Detection0
Sequential Anomaly Detection using Inverse Reinforcement Learning0
Estimate the Implicit Likelihoods of GANs with Application to Anomaly DetectionCode0
Pseudo-healthy synthesis with pathology disentanglement and adversarial learningCode0
Network Anomaly Detection based on Tensor Decomposition0
DriftNet: Aggressive Driving Behavior Classification using 3D EfficientNet ArchitectureCode0
Outlier detection at the parcel-level in wheat and rapeseed crops using multispectral and SAR time series0
Exploring time-series motifs through DTW-SOM0
Motion and Region Aware Adversarial Learning for Fall Detection with Thermal ImagingCode1
Old is Gold: Redefining the Adversarially Learned One-Class Classifier Training ParadigmCode1
Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model TuningCode0
Continual Learning for Anomaly Detection in Surveillance Videos0
Contextual-Bandit Anomaly Detection for IoT Data in Distributed Hierarchical Edge Computing0
Anomaly Detection in Trajectory Data with Normalizing Flows0
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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