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 3001–3025 of 4856 papers

TitleStatusHype
Robust Learning of Deep Time Series Anomaly Detection Models with Contaminated Training Data—0
Robust PCA for Anomaly Detection and Data Imputation in Seasonal Time Series—0
Control theoretically explainable application of autoencoder methods to fault detection in nonlinear dynamic systems—0
Curved Geometric Networks for Visual Anomaly Recognition—0
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions—0
A One-Class Classification method based on Expanded Non-Convex HullsCode0
Scrutinizing Shipment Records To Thwart Illegal Timber Trade—0
Robust Rayleigh Regression Method for SAR Image Processing in Presence of Outliers—0
Concept Drift Challenge in Multimedia Anomaly Detection: A Case Study with Facial Datasets—0
Learning Appearance-motion Normality for Video Anomaly Detection—0
Look at Adjacent Frames: Video Anomaly Detection without Offline Training—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Exploring the Design of Adaptation Protocols for Improved Generalization and Machine Learning Safety—0
Series2Graph: Graph-based Subsequence Anomaly Detection for Time Series—0
Hierarchical Semi-Supervised Contrastive Learning for Contamination-Resistant Anomaly DetectionCode0
e-G2C: A 0.14-to-8.31 μJ/Inference NN-based Processor with Continuous On-chip Adaptation for Anomaly Detection and ECG Conversion from EGM—0
A general-purpose method for applying Explainable AI for Anomaly Detection—0
Anomaly Detection for Fraud in Cryptocurrency Time Series—0
Comparative Study on Supervised versus Semi-supervised Machine Learning for Anomaly Detection of In-vehicle CAN Network—0
A Hybrid Convolutional Neural Network with Meta Feature Learning for Abnormality Detection in Wireless Capsule Endoscopy Images—0
Anomaly Detection of Smart Metering System for Power Management with Battery Storage System/Electric Vehicle—0
Digraphwave: Scalable Extraction of Structural Node Embeddings via Diffusion on Directed Graphs—0
Unsupervised Industrial Anomaly Detection via Pattern Generative and Contrastive Networks—0
Using Anomaly Detection to Detect Poisoning Attacks in Federated Learning Applications—0
RESAM: Requirements Elicitation and Specification for Deep-Learning Anomaly Models with Applications to UAV Flight Controllers—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