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

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Showing 201–225 of 4856 papers

TitleStatusHype
Blockchain Meets Adaptive Honeypots: A Trust-Aware Approach to Next-Gen IoT Security—0
Explainable Unsupervised Anomaly Detection with Random Forest—0
GenCLIP: Generalizing CLIP Prompts for Zero-shot Anomaly Detection—0
Memory-Augmented Dual-Decoder Networks for Multi-Class Unsupervised Anomaly Detection—0
M^2AD: Multi-Sensor Multi-System Anomaly Detection through Global Scoring and Calibrated ThresholdingCode0
Advancing Video Anomaly Detection: A Bi-Directional Hybrid Framework for Enhanced Single- and Multi-Task Approaches—0
Uncovering Issues in the Radio Access Network by Looking at the Neighbors—0
Pets: General Pattern Assisted Architecture For Time Series Analysis—0
Towards Anomaly-Aware Pre-Training and Fine-Tuning for Graph Anomaly Detection—0
Real-IAD D3: A Real-World 2D/Pseudo-3D/3D Dataset for Industrial Anomaly Detection—0
Balancing Privacy and Action Performance: A Penalty-Driven Approach to Image Anonymization—0
DConAD: A Differencing-based Contrastive Representation Learning Framework for Time Series Anomaly DetectionCode0
Can Local Representation Alignment RNNs Solve Temporal Tasks?—0
Enhancing Pothole Detection and Characterization: Integrated Segmentation and Depth Estimation in Road Anomaly Systems—0
Chain-of-Thought Textual Reasoning for Few-shot Temporal Action Localization—0
Towards a Multi-Agent Vision-Language System for Zero-Shot Novel Hazardous Object Detection for Autonomous Driving SafetyCode0
Sliced-Wasserstein Distance-based Data Selection—0
LAD-Reasoner: Tiny Multimodal Models are Good Reasoners for Logical Anomaly Detection—0
EventVAD: Training-Free Event-Aware Video Anomaly Detection—0
MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection—0
HSS-IAD: A Heterogeneous Same-Sort Industrial Anomaly Detection DatasetCode0
Predicting BVD Re-emergence in Irish Cattle From Highly Imbalanced Herd-Level Data Using Machine Learning Algorithms—0
Quorum: Zero-Training Unsupervised Anomaly Detection using Quantum AutoencodersCode0
3D-PNAS: 3D Industrial Surface Anomaly Synthesis with Perlin Noise—0
ACMamba: Fast Unsupervised Anomaly Detection via An Asymmetrical Consensus State Space Model—0
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All datasetsMVTec ADVisAMVTec LOCO ADOne-class CIFAR-10CUHK AvenueShanghaiTechFishyscapes L&FUCR Anomaly ArchiveMPDDBTADOne-class CIFAR-100UBnormal

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016—Unverified
2CPR-fast(TensorRT)FPS362—Unverified
3CPR(TensorRT)FPS130—Unverified
4GLASSDetection AUROC99.9—Unverified
5UniNetDetection AUROC99.9—Unverified
6HETMMDetection AUROC99.8—Unverified
7INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8—Unverified
8EfficientAD (early stopping)Detection AUROC99.8—Unverified
9DDADDetection AUROC99.8—Unverified
10PBASDetection AUROC99.8—Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8—Unverified
2GLADDetection AUROC99.5—Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15—Unverified
4INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9—Unverified
5DDADDetection AUROC98.9—Unverified
6Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9—Unverified
7DiffusionADDetection AUROC98.8—Unverified
8GLASSDetection AUROC98.8—Unverified
9TransFusionDetection AUROC98.7—Unverified
10HETMMDetection AUROC98.1—Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3—Unverified
2PSADAvg. Detection AUROC94.9—Unverified