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 21512175 of 4856 papers

TitleStatusHype
Unsupervised Anomaly Detection with Local-Sensitive VQVAE and Global-Sensitive Transformers0
Improving Object Detection in Medical Image Analysis through Multiple Expert Annotators: An Empirical Investigation0
GAT-COBO: Cost-Sensitive Graph Neural Network for Telecom Fraud DetectionCode1
Protecting Federated Learning from Extreme Model Poisoning Attacks via Multidimensional Time Series Anomaly Detection0
Searching for long faint astronomical high energy transients: a data driven approachCode1
Attention Boosted Autoencoder for Building Energy Anomaly Detection0
Hard-normal Example-aware Template Mutual Matching for Industrial Anomaly DetectionCode1
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection0
SimpleNet: A Simple Network for Image Anomaly Detection and LocalizationCode2
WinCLIP: Zero-/Few-Shot Anomaly Classification and SegmentationCode2
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level LatenciesCode2
Topological Pooling on GraphsCode0
Anomaly Detection under Distribution ShiftCode1
Interpretable Anomaly Detection via Discrete Optimization0
Failure-tolerant Distributed Learning for Anomaly Detection in Wireless Networks0
Confidence-Aware and Self-Supervised Image Anomaly LocalisationCode0
Complementary Pseudo Multimodal Feature for Point Cloud Anomaly DetectionCode1
Hierarchical Semantic Contrast for Scene-aware Video Anomaly Detection0
Unbiased Multiple Instance Learning for Weakly Supervised Video Anomaly DetectionCode1
TSI-GAN: Unsupervised Time Series Anomaly Detection using Convolutional Cycle-Consistent Generative Adversarial NetworksCode1
One-Step Detection Paradigm for Hyperspectral Anomaly Detection via Spectral Deviation Relationship LearningCode1
Anomaly Detection in Aeronautics Data with Quantum-compatible Discrete Deep Generative Model0
A Novel Multi-Stage Approach for Hierarchical Intrusion DetectionCode0
Dens-PU: PU Learning with Density-Based Positive Labeled Augmentation0
Defect Detection Approaches Based on Simulated Reference Image0
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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