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 1051–1075 of 4856 papers

TitleStatusHype
Autoencoder based Anomaly Detection and Explained Fault Localization in Industrial Cooling Systems—0
A Formal Framework for Assessing and Mitigating Emergent Security Risks in Generative AI Models: Bridging Theory and Dynamic Risk Mitigation—0
Anomaly Detection based on Zero-Shot Outlier Synthesis and Hierarchical Feature Distillation—0
Activity-Guided Industrial Anomalous Sound Detection against Interferences—0
A Unifying Review of Deep and Shallow Anomaly Detection—0
Anomaly Detection Based on Unsupervised Disentangled Representation Learning in Combination with Manifold Learning—0
A flexible outlier detector based on a topology given by graph communities—0
Abnormal Event Detection In Videos Using Deep Embedding—0
Comparison of RNN Encoder-Decoder Models for Anomaly Detection—0
A Unified Simulation Framework for Visual and Behavioral Fidelity in Crowd Analysis—0
Anomaly Detection Based on Selection and Weighting in Latent Space—0
A Unified Off-Policy Evaluation Approach for General Value Function—0
Anomaly Detection Based on Multiple-Hypothesis Autoencoder—0
Abnormal Event Detection in Videos using Generative Adversarial Nets—0
A Unified Latent Schrodinger Bridge Diffusion Model for Unsupervised Anomaly Detection and Localization—0
Anomaly Detection Based on Isolation Mechanisms: A Survey—0
A Unified Framework for Context-Aware IoT Management and State-of-the-Art IoT Traffic Anomaly Detection—0
Anomaly Detection Based on Indicators Aggregation—0
Active Rule Mining for Multivariate Anomaly Detection in Radio Access Networks—0
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization—0
Augment to Detect Anomalies with Continuous Labelling—0
Augmentation based unsupervised domain adaptation—0
Anomaly Detection Based on Generalized Gaussian Distribution approach for Ultra-Wideband (UWB) Indoor Positioning System—0
Auditing Keyword Queries Over Text Documents—0
Anomaly Detection Based on Deep Learning Using Video for Prevention of Industrial Accidents—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