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 2451–2475 of 4856 papers

TitleStatusHype
Rethinking Contrastive Learning in Graph Anomaly Detection: A Clean-View Perspective—0
Rethinking Metrics and Benchmarks of Video Anomaly Detection—0
Rethinking Video Anomaly Detection - A Continual Learning Approach—0
Retrieval Augmented Anomaly Detection (RAAD): Nimble Model Adjustment Without Retraining—0
Detection of Backdoors in Trained Classifiers Without Access to the Training Set—0
Reversing the Abnormal: Pseudo-Healthy Generative Networks for Anomaly Detection—0
Revisited Large Language Model for Time Series Analysis through Modality Alignment—0
Revisiting DDIM Inversion for Controlling Defect Generation by Disentangling the Background—0
Revisiting randomized choices in isolation forests—0
Revitalizing Reconstruction Models for Multi-class Anomaly Detection via Class-Aware Contrastive Learning—0
ReX: A Framework for Incorporating Temporal Information in Model-Agnostic Local Explanation Techniques—0
RGI: robust GAN-inversion for mask-free image inpainting and unsupervised pixel-wise anomaly detection—0
Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants—0
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning—0
Road Obstacle Detection based on Unknown Objectness Scores—0
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift—0
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset—0
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks—0
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models—0
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy—0
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning—0
Robust Anomaly Detection for Time-series Data—0
Robust Anomaly Detection in Images using Adversarial Autoencoders—0
Robust Anomaly Detection in Network Traffic: Evaluating Machine Learning Models on CICIDS2017—0
Robust Anomaly Detection Using Semidefinite Programming—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