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 51–100 of 4856 papers

TitleStatusHype
Data Driven Diagnosis for Large Cyber-Physical-Systems with Minimal Prior Information—0
IQE-CLIP: Instance-aware Query Embedding for Zero-/Few-shot Anomaly Detection in Medical DomainCode0
Conditional diffusion models for guided anomaly detection in brain images using fluid-driven anomaly randomization—0
Wavelet Scattering Transform and Fourier Representation for Offline Detection of Malicious Clients in Federated Learning—0
An Explainable Deep Learning Framework for Brain Stroke and Tumor Progression via MRI Interpretation—0
Integrating Asynchronous AdaBoost into Federated Learning: Five Real World Applications—0
PatchGuard: Adversarially Robust Anomaly Detection and Localization through Vision Transformers and Pseudo AnomaliesCode1
MAMBO: High-Resolution Generative Approach for Mammography Images—0
HomographyAD: Deep Anomaly Detection Using Self Homography Learning—0
Context-aware TFL: A Universal Context-aware Contrastive Learning Framework for Temporal Forgery Localization—0
Cross-Entropy Games for Language Models: From Implicit Knowledge to General Capability Measures—0
MLOps with Microservices: A Case Study on the Maritime Domain—0
Noise-Driven AI Sensors: Secure Healthcare Monitoring with PUFs—0
Track Any Anomalous Object: A Granular Video Anomaly Detection Pipeline—0
Federated Isolation Forest for Efficient Anomaly Detection on Edge IoT Systems—0
INP-Former++: Advancing Universal Anomaly Detection via Intrinsic Normal Prototypes and Residual LearningCode3
An AI-Based Public Health Data Monitoring System—0
System Calls for Malware Detection and Classification: Methodologies and Applications—0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series—0
Generator Based Inference (GBI)Code0
Tensor Network for Anomaly Detection in the Latent Space of Proton Collision Events at the LHCCode0
KairosAD: A SAM-Based Model for Industrial Anomaly Detection on Embedded DevicesCode0
Bridging 3D Anomaly Localization and Repair via High-Quality Continuous Geometric RepresentationCode0
SentinelAgent: Graph-based Anomaly Detection in Multi-Agent Systems—0
HLSAD: Hodge Laplacian-based Simplicial Anomaly Detection—0
Anomaly Detection and Improvement of Clusters using Enhanced K-Means Algorithm—0
Machine Learning-Based Anomaly Detection of Correlated Sensor Data: An Integrated Principal Component Analysis-Autoencoder Approach—0
VAU-R1: Advancing Video Anomaly Understanding via Reinforcement Fine-TuningCode2
Sentinel: Scheduling Live Streams with Proactive Anomaly Detection in Crowdsourced Cloud-Edge Platforms—0
FreRA: A Frequency-Refined Augmentation for Contrastive Learning on Time Series ClassificationCode1
Distributed Federated Learning for Vehicular Network Security: Anomaly Detection Benefits and Multi-Domain Attack Threats—0
OmniAD: Detect and Understand Industrial Anomaly via Multimodal Reasoning—0
Anomalies by Synthesis: Anomaly Detection using Generative Diffusion Models for Off-Road Navigation—0
Is Hyperbolic Space All You Need for Medical Anomaly Detection?—0
Mentor3AD: Feature Reconstruction-based 3D Anomaly Detection via Multi-modality Mentor Learning—0
Fog Intelligence for Network Anomaly Detection—0
RoBiS: Robust Binary Segmentation for High-Resolution Industrial ImagesCode1
Zero-Trust Foundation Models: A New Paradigm for Secure and Collaborative Artificial Intelligence for Internet of Things—0
Byzantine-Resilient Distributed P2P Energy Trading via Spatial-Temporal Anomaly Detection—0
Cellwise and Casewise Robust Covariance in High Dimensions—0
SuperAD: A Training-free Anomaly Classification and Segmentation Method for CVPR 2025 VAND 3.0 Workshop Challenge Track 1: Adapt & Detect—0
Vad-R1: Towards Video Anomaly Reasoning via Perception-to-Cognition Chain-of-ThoughtCode1
eACGM: Non-instrumented Performance Tracing and Anomaly Detection towards Machine Learning Systems—0
Words as Geometric Features: Estimating Homography using Optical Character Recognition as Compressed Image Representation—0
Rethinking Metrics and Benchmarks of Video Anomaly Detection—0
Chi-Square Wavelet Graph Neural Networks for Heterogeneous Graph Anomaly DetectionCode0
Anomaly detection in radio galaxy data with trainable COSFIRE filtersCode0
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Center-aware Residual Anomaly Synthesis for Multi-class Industrial Anomaly DetectionCode1
Learning Normal Patterns in Musical Loops—0
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Benchmark Results

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