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 40014025 of 4856 papers

TitleStatusHype
MIM-Based GAN: Information Metric to Amplify Small Probability Events Importance in Generative Adversarial Networks0
Unsupervised Anomaly Detection with Adversarial Mirrored AutoEncodersCode1
Attention-Based Self-Supervised Feature Learning for Security Data0
Dividing Deep Learning Model for Continuous Anomaly Detection of Inconsistent ICT Systems0
robROSE: A robust approach for dealing with imbalanced data in fraud detectionCode0
Synthesize then Compare: Detecting Failures and Anomalies for Semantic SegmentationCode1
Self-Supervised Log ParsingCode2
Anomaly Detection in Video Data Based on Probabilistic Latent Space Models0
Breast Cancer Detection Using Convolutional Neural NetworksCode0
Optimal Image Smoothing and Its Applications in Anomaly Detection in Remote Sensing0
Anomalous Example Detection in Deep Learning: A Survey0
Self-trained Deep Ordinal Regression for End-to-End Video Anomaly Detection0
Hybrid Cryptocurrency Pump and Dump Detection0
Fast Distance-based Anomaly Detection in Images Using an Inception-like AutoencoderCode1
Building and Interpreting Deep Similarity ModelsCode0
Anomaly Detection in Beehives using Deep Recurrent Autoencoders0
Isolation Mondrian Forest for Batch and Online Anomaly DetectionCode1
Hardware Architecture Proposal for TEDA algorithm to Data Streaming Anomaly Detection0
Machine Learning based Anomaly Detection for 5G Networks0
A^3: Activation Anomaly AnalysisCode1
CRATOS: Cognition of Reliable Algorithm for Time-series Optimal Solution0
Why is the Mahalanobis Distance Effective for Anomaly Detection?0
Unsupervised Dictionary Learning for Anomaly Detection0
DROCC: Deep Robust One-Class Classification0
MLography: An Automated Quantitative Metallography Model for Impurities Anomaly Detection using Novel Data Mining and Deep Learning ApproachCode1
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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