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 2726–2750 of 4856 papers

TitleStatusHype
Synthetic User Behavior Sequence Generation with Large Language Models for Smart Homes—0
Systematic Review: Anomaly Detection in Connected and Autonomous Vehicles—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
TAAD: Time-varying adversarial anomaly detection in dynamic graphs—0
TabADM: Unsupervised Tabular Anomaly Detection with Diffusion Models—0
TAB: Text-Align Anomaly Backbone Model for Industrial Inspection Tasks—0
Tackling Diverse Minorities in Imbalanced Classification—0
TAD-Bench: A Comprehensive Benchmark for Embedding-Based Text Anomaly Detection—0
TADPOLE: Task ADapted Pre-Training via AnOmaLy DEtection—0
TAGA: Self-supervised Learning for Template-free Animatable Gaussian Articulated Model—0
Tail of Distribution GAN (TailGAN): Generative-Adversarial-Network-Based Boundary Formation—0
Tailoring Machine Learning for Process Mining—0
Taming Anomalies with Down-Up Sampling Networks: Group Center Preserving Reconstruction for 3D Anomaly Detection—0
Targeted collapse regularized autoencoder for anomaly detection: black hole at the center—0
Task Agnostic and Post-hoc Unseen Distribution Detection—0
Task-agnostic Continual Learning with Hybrid Probabilistic Models—0
Task-aware Similarity Learning for Event-triggered Time Series—0
Task-Oriented Connectivity for Networked Robotics with Generative AI and Semantic Communications—0
Task-Oriented Optimal Sequencing of Visualization Charts—0
TauAD: MRI-free Tau Anomaly Detection in PET Imaging via Conditioned Diffusion Models—0
Taurus: A Data Plane Architecture for Per-Packet ML—0
Teacher Encoder-Student Decoder Denoising Guided Segmentation Network for Anomaly Detection—0
Teacher-Student Network for 3D Point Cloud Anomaly Detection with Few Normal Samples—0
TeG: Temporal-Granularity Method for Anomaly Detection with Attention in Smart City Surveillance—0
Template-guided Hierarchical Feature Restoration for 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