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 701725 of 4856 papers

TitleStatusHype
Graph Contrastive Learning for Anomaly DetectionCode1
DRAEM -- A discriminatively trained reconstruction embedding for surface anomaly detectionCode1
A Hybrid Video Anomaly Detection Framework via Memory-Augmented Flow Reconstruction and Flow-Guided Frame PredictionCode1
P-WAE: Generalized Patch-Wasserstein Autoencoder for Anomaly ScreeningCode1
Unsupervised Image Anomaly Detection and Segmentation Based on Pre-trained Feature MappingCode1
Log-based Anomaly Detection Without Log ParsingCode1
Explainable Deep Few-shot Anomaly Detection with Deviation NetworksCode1
CFLOW-AD: Real-Time Unsupervised Anomaly Detection with Localization via Conditional Normalizing FlowsCode1
Standardized Max Logits: A Simple yet Effective Approach for Identifying Unexpected Road Obstacles in Urban-Scene SegmentationCode1
Traffic4D: Single View Reconstruction of Repetitious Activity Using Longitudinal Self-SupervisionCode1
Neural Contextual Anomaly Detection for Time SeriesCode1
Experience Report: Deep Learning-based System Log Analysis for Anomaly DetectionCode1
Enhancing the Analysis of Software Failures in Cloud Computing Systems with Deep LearningCode1
Deep Learning in Latent Space for Video Prediction and CompressionCode1
TS2Vec: Towards Universal Representation of Time SeriesCode1
Anomaly Detection in Dynamic Graphs via TransformerCode1
FastAno: Fast Anomaly Detection via Spatio-temporal Patch TransformationCode1
Anomaly Detection in Video Sequences: A Benchmark and Computational ModelCode1
Federated Learning for Internet of Things: A Federated Learning Framework for On-device Anomaly Data DetectionCode1
Time Series Anomaly Detection for Cyber-Physical Systems via Neural System Identification and Bayesian FilteringCode1
A Comprehensive Survey on Graph Anomaly Detection with Deep LearningCode1
MLPerf Tiny BenchmarkCode1
Graph Neural Network-Based Anomaly Detection in Multivariate Time SeriesCode1
DASVDD: Deep Autoencoding Support Vector Data Descriptor for Anomaly DetectionCode1
XBNet : An Extremely Boosted Neural NetworkCode1
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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