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 1926–1950 of 4856 papers

TitleStatusHype
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
Fractals as Pre-training Datasets for Anomaly Detection and Localization—0
Attend, Distill, Detect: Attention-aware Entropy Distillation for Anomaly DetectionCode0
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
TS3IM: Unveiling Structural Similarity in Time Series through Image Similarity Assessment Insights—0
Anomaly Detection in Graph Structured Data: A Survey—0
Exploiting Autoencoder's Weakness to Generate Pseudo Anomalies—0
Artificial intelligence for abnormality detection in high volume neuroimaging: a systematic review and meta-analysis—0
Dual-Image Enhanced CLIP for Zero-Shot Anomaly Detection—0
Discrepancy-based Diffusion Models for Lesion Detection in Brain MRI—0
Anomaly Detection in Certificate Transparency Logs—0
Less-supervised learning with knowledge distillation for sperm morphology analysisCode0
A Reliable Framework for Human-in-the-Loop Anomaly Detection in Time Series—0
Braced Fourier Continuation and Regression for Anomaly DetectionCode0
A self-supervised text-vision framework for automated brain abnormality detection—0
AnoGAN for Tabular Data: A Novel Approach to Anomaly Detection—0
A Model-Free Kullback-Leibler Divergence Filter for Anomaly Detection in Noisy Data Series—0
GAD: A Real-time Gait Anomaly Detection System with Online Adaptive Learning—0
A Data Mining-Based Dynamical Anomaly Detection Method for Integrating with an Advance Metering System—0
Generic Multi-modal Representation Learning for Network Traffic Analysis—0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
Subgraph2vec: A random walk-based algorithm for embedding knowledge graphs—0
An Attention-Based Deep Generative Model for Anomaly Detection in Industrial Control Systems—0
CVTGAD: Simplified Transformer with Cross-View Attention for Unsupervised Graph-level Anomaly DetectionCode0
Interpretable Data-driven Anomaly Detection in Industrial Processes with ExIFFI—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