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 351–375 of 4856 papers

TitleStatusHype
X2CT-CLIP: Enable Multi-Abnormality Detection in Computed Tomography from Chest Radiography via Tri-Modal Contrastive Learning—0
Memory Efficient Continual Learning for Edge-Based Visual Anomaly Detection—0
Exploring Intrinsic Normal Prototypes within a Single Image for Universal Anomaly DetectionCode3
Anomaly detection in non-stationary videos using time-recursive differencing network based prediction—0
RAAD-LLM: Adaptive Anomaly Detection Using LLMs and RAG Integration—0
World Models for Anomaly Detection during Model-Based Reinforcement Learning Inference—0
Network Anomaly Detection for IoT Using Hyperdimensional Computing on NSL-KDD—0
Building Machine Learning Challenges for Anomaly Detection in Science—0
OIPR: Evaluation for Time-series Anomaly Detection Inspired by Operator InterestCode0
Meta Learning-Driven Iterative Refinement for Robust Anomaly Detection in Industrial Inspection—0
PA-CLIP: Enhancing Zero-Shot Anomaly Detection through Pseudo-Anomaly Awareness—0
Fence Theorem: Preprocessing is Dual-Objective Semantic Structure Isolator in 3D Anomaly Detection—0
Language-Assisted Feature Transformation for Anomaly DetectionCode0
Transformer Based Self-Context Aware Prediction for Few-Shot Anomaly Detection in Videos—0
CyberCScope: Mining Skewed Tensor Streams and Online Anomaly Detection in Cybersecurity Systems—0
G-OSR: A Comprehensive Benchmark for Graph Open-Set Recognition—0
UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly DetectionCode2
When Unsupervised Domain Adaptation meets One-class Anomaly Detection: Addressing the Two-fold Unsupervised Curse by Leveraging Anomaly Scarcity—0
Detection of anomalies in cow activity using wavelet transform based features—0
FedDyMem: Efficient Federated Learning with Dynamic Memory and Memory-Reduce for Unsupervised Image Anomaly Detection—0
Detecting Crypto Pump-and-Dump Schemes: A Thresholding-Based Approach to Handling Market Noise—0
One-for-More: Continual Diffusion Model for Anomaly DetectionCode2
Discovering Antagonists in Networks of Systems: Robot DeploymentCode0
Time-MQA: Time Series Multi-Task Question Answering with Context Enhancement—0
HDM: Hybrid Diffusion Model for Unified Image Anomaly Detection—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