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 826850 of 4856 papers

TitleStatusHype
USAD: UnSupervised Anomaly Detection on Multivariate Time SeriesCode1
TAnoGAN: Time Series Anomaly Detection with Generative Adversarial NetworksCode1
Localizing Anomalies from Weakly-Labeled VideosCode1
Enhancing Graph Neural Network-based Fraud Detectors against Camouflaged FraudstersCode1
Anomaly localization by modeling perceptual featuresCode1
ARCADe: A Rapid Continual Anomaly DetectorCode1
Encoding Structure-Texture Relation with P-Net for Anomaly Detection in Retinal ImagesCode1
A General Framework For Detecting Anomalous Inputs to DNN ClassifiersCode1
DeScarGAN: Disease-Specific Anomaly Detection with Weak SupervisionCode1
Fast Unsupervised Anomaly Detection in Traffic VideosCode1
Weakly and Partially Supervised Learning Frameworks for Anomaly DetectionCode1
Interpretable Anomaly Detection with DIFFI: Depth-based Isolation Forest Feature ImportanceCode1
Backpropagated Gradient Representations for Anomaly DetectionCode1
CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted InstancesCode1
Few-shot Scene-adaptive Anomaly DetectionCode1
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device FailureCode1
Anomaly Detection-Based Unknown Face Presentation Attack DetectionCode1
Few-Shot One-Class Classification via Meta-LearningCode1
Explainable Deep One-Class ClassificationCode1
Laplacian Change Point Detection for Dynamic GraphsCode1
Subject-Aware Contrastive Learning for BiosignalsCode1
Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion DetectionCode1
Patch SVDD: Patch-level SVDD for Anomaly Detection and SegmentationCode1
Few-Shot Anomaly Detection for Polyp Frames from ColonoscopyCode1
Efficient Deep CNN-BiLSTM Model for Network Intrusion DetectionCode1
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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