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 726750 of 4856 papers

TitleStatusHype
XBNet : An Extremely Boosted Neural NetworkCode1
Sketch-Based Anomaly Detection in Streaming GraphsCode1
MemStream: Memory-Based Streaming Anomaly DetectionCode1
Mean-Shifted Contrastive Loss for Anomaly DetectionCode1
ToyADMOS2: Another dataset of miniature-machine operating sounds for anomalous sound detection under domain shift conditionsCode1
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly SegmentationCode1
Conformal Anomaly Detection on Spatio-Temporal Observations with Missing DataCode1
Feature Encoding with AutoEncoders for Weakly-supervised Anomaly DetectionCode1
Masked Contrastive Learning for Anomaly DetectionCode1
Vision Transformers are Robust LearnersCode1
Real-Time Anomaly Detection and Feature Analysis Based on Time Series for Surveillance VideoCode1
Unsupervised Offline Changepoint Detection EnsemblesCode1
A Hierarchical Transformation-Discriminating Generative Model for Few Shot Anomaly DetectionCode1
Inpainting Transformer for Anomaly DetectionCode1
The 5th AI City ChallengeCode1
Anomaly Detection for Solder Joints Using β-VAECode1
Supervised Anomaly Detection via Conditional Generative Adversarial Network and Ensemble Active LearningCode1
An End-to-End Computer Vision Methodology for Quantitative MetallographyCode1
A Lightweight Concept Drift Detection and Adaptation Framework for IoT Data StreamsCode1
VT-ADL: A Vision Transformer Network for Image Anomaly Detection and LocalizationCode1
What is Wrong with One-Class Anomaly Detection?Code1
Beyond Outlier Detection: Outlier Interpretation by Attention-Guided Triplet Deviation NetworkCode1
Weakly Supervised Video Anomaly Detection via Center-guided Discriminative LearningCode1
ADNet: Temporal Anomaly Detection in Surveillance VideosCode1
Learning Normal Dynamics in Videos with Meta Prototype 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