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 21762200 of 4856 papers

TitleStatusHype
Unlocking Layer-wise Relevance Propagation for Autoencoders0
Focus or Not: A Baseline for Anomaly Event Detection On the Open Public Places with Satellite ImagesCode0
Did You Train on My Dataset? Towards Public Dataset Protection with Clean-Label Backdoor WatermarkingCode0
PseudoBound: Limiting the anomaly reconstruction capability of one-class classifiers using pseudo anomalies0
GADformer: A Transparent Transformer Model for Group Anomaly Detection on TrajectoriesCode0
A Bi-LSTM Autoencoder Framework for Anomaly Detection -- A Case Study of a Wind Power Dataset0
Wireless Sensor Networks anomaly detection using Machine Learning: A Survey0
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection0
DiffusionAD: Norm-guided One-step Denoising Diffusion for Anomaly DetectionCode2
Towards Phytoplankton Parasite Detection Using AutoencodersCode0
Lifelong Continual Learning for Anomaly Detection: New Challenges, Perspectives, and InsightsCode0
Network Anomaly Detection Using Federated Learning0
Spacecraft Anomaly Detection with Attention Temporal Convolution NetworkCode1
Hallucinated Heartbeats: Anomaly-Aware Remote Pulse EstimationCode0
Interpretable Outlier Summarization0
Anomaly Detection with Ensemble of Encoder and Decoder0
3D Masked Autoencoders with Application to Anomaly Detection in Non-Contrast Enhanced Breast MRI0
Learning Global-Local Correspondence with Semantic Bottleneck for Logical Anomaly Detection0
Deep Anomaly Detection on Tennessee Eastman Process Data0
Adapting Contrastive Language-Image Pretrained (CLIP) Models for Out-of-Distribution DetectionCode0
Automated visual inspection of CMS HGCAL silicon sensor surface using an ensemble of a deep convolutional autoencoder and classifier0
Multi-level Memory-augmented Appearance-Motion Correspondence Framework for Video Anomaly Detection0
Diversity-Measurable Anomaly DetectionCode1
Updated version: A Video Anomaly Detection Framework based on Appearance-Motion Semantics Representation Consistency0
Learning Representation for Anomaly Detection of Vehicle Trajectories0
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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