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 39513975 of 4856 papers

TitleStatusHype
CHASE: A Causal Heterogeneous Graph based Framework for Root Cause Analysis in Multimodal Microservice Systems0
Chili Pepper Disease Diagnosis via Image Reconstruction Using GrabCut and Generative Adversarial Serial Autoencoder0
Chimera: Effectively Modeling Multivariate Time Series with 2-Dimensional State Space Models0
CICADA: Cross-Domain Interpretable Coding for Anomaly Detection and Adaptation in Multivariate Time Series0
CIoTA: Collaborative IoT Anomaly Detection via Blockchain0
Circuit design in biology and machine learning. II. Anomaly detection0
Class Augmented Semi-Supervised Learning for Practical Clinical Analytics on Physiological Signals0
Classification of Anomalies in Telecommunication Network KPI Time Series0
Classification Tree Diagrams in Health Informatics Applications0
Class Imbalance in Anomaly Detection: Learning from an Exactly Solvable Model0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection0
CLAWS: Contrastive Learning with hard Attention and Weak Supervision0
CL-BioGAN: Biologically-Inspired Cross-Domain Continual Learning for Hyperspectral Anomaly Detection0
CL-CaGAN: Capsule differential adversarial continuous learning for cross-domain hyperspectral anomaly detection0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection0
Clear Memory-Augmented Auto-Encoder for Surface Defect Detection0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection0
Client-Specific Anomaly Detection for Face Presentation Attack Detection0
CLIP3D-AD: Extending CLIP for 3D Few-Shot Anomaly Detection with Multi-View Images Generation0
CLIP-AD: A Language-Guided Staged Dual-Path Model for Zero-shot Anomaly Detection0
Cloud-Based AI Systems: Leveraging Large Language Models for Intelligent Fault Detection and Autonomous Self-Healing0
CloudRCA: A Root Cause Analysis Framework for Cloud Computing Platforms0
Cluster-Aware Causal Mixer for Online Anomaly Detection in Multivariate Time Series0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos0
Clustering and Unsupervised Anomaly Detection with L2 Normalized Deep Auto-Encoder Representations0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1CPR-faster(TensorRT)FPS1,016Unverified
2CPR-fast(TensorRT)FPS362Unverified
3CPR(TensorRT)FPS130Unverified
4GLASSDetection AUROC99.9Unverified
5UniNetDetection AUROC99.9Unverified
6INP-Fomer ViT-L (model-unified multi-class)Detection AUROC99.8Unverified
7DDADDetection AUROC99.8Unverified
8EfficientAD (early stopping)Detection AUROC99.8Unverified
9PBASDetection AUROC99.8Unverified
10HETMMDetection AUROC99.8Unverified
#ModelMetricClaimedVerifiedStatus
1UniNetDetection AUROC99.8Unverified
2GLADDetection AUROC99.5Unverified
3UniNet(model-unified multi-class)Detection AUROC99.15Unverified
4DDADDetection AUROC98.9Unverified
5Dinomaly ViT-L (model-unified multi-class)Detection AUROC98.9Unverified
6INP-Former ViT-B (model-unified multi-class)Detection AUROC98.9Unverified
7DiffusionADDetection AUROC98.8Unverified
8GLASSDetection AUROC98.8Unverified
9TransFusionDetection AUROC98.7Unverified
10HETMMDetection AUROC98.1Unverified
#ModelMetricClaimedVerifiedStatus
1CSADAvg. Detection AUROC95.3Unverified
2PSADAvg. Detection AUROC94.9Unverified