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

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 2961–2970 of 4856 papers

Title	Date	Tasks	Status
Risk-Based Thresholding for Reliable Anomaly Detection in Concentrated Solar Power Plants	Mar 24, 2025	Anomaly Detection	—Unverified
RLAD: Time Series Anomaly Detection through Reinforcement Learning and Active Learning	Mar 31, 2021	Active LearningAnomaly Detection	—Unverified
Road Obstacle Detection based on Unknown Objectness Scores	Mar 27, 2024	Anomaly DetectionAutonomous Driving	—Unverified
ROADS: Robust Prompt-driven Multi-Class Anomaly Detection under Domain Shift	Nov 25, 2024	Anomaly DetectionMulti-class Anomaly Detection	—Unverified
A Comprehensive Guide to CAN IDS Data & Introduction of the ROAD Dataset	Dec 29, 2020	Anomaly DetectionBenchmarking	—Unverified
Robust and Computationally-Efficient Anomaly Detection using Powers-of-Two Networks	Oct 30, 2019	Anomaly DetectionDenoising	—Unverified
Robust and Transferable Anomaly Detection in Log Data using Pre-Trained Language Models	Feb 23, 2021	Anomaly Detection	—Unverified
Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy	Nov 16, 2019	Anomaly DetectionBackdoor Attack	—Unverified
Robust Anomaly Detection for Particle Physics Using Multi-Background Representation Learning	Jan 16, 2024	Anomaly DetectionOut-of-Distribution Detection	—Unverified
Robust Anomaly Detection for Time-series Data	Feb 6, 2022	Anomaly DetectionTime Series	—Unverified

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All datasets MVTec AD VisA MVTec LOCO AD One-class CIFAR-10 CUHK Avenue ShanghaiTech Fishyscapes L&F UCR Anomaly Archive MPDD BTAD One-class CIFAR-100 UBnormal

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	CPR-faster(TensorRT)	FPS	1,016	—	Unverified
2	CPR-fast(TensorRT)	FPS	362	—	Unverified
3	CPR(TensorRT)	FPS	130	—	Unverified
4	UniNet	Detection AUROC	99.9	—	Unverified
5	GLASS	Detection AUROC	99.9	—	Unverified
6	PBAS	Detection AUROC	99.8	—	Unverified
7	HETMM	Detection AUROC	99.8	—	Unverified
8	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
9	DDAD	Detection AUROC	99.8	—	Unverified
10	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	UniNet	Detection AUROC	99.8	—	Unverified
2	GLAD	Detection AUROC	99.5	—	Unverified
3	UniNet(model-unified multi-class)	Detection AUROC	99.15	—	Unverified
4	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
5	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	DDAD	Detection AUROC	98.9	—	Unverified
7	GLASS	Detection AUROC	98.8	—	Unverified
8	DiffusionAD	Detection AUROC	98.8	—	Unverified
9	TransFusion	Detection AUROC	98.7	—	Unverified
10	HETMM	Detection AUROC	98.1	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CSAD	Avg. Detection AUROC	95.3	—	Unverified
2	PSAD	Avg. Detection AUROC	94.9	—	Unverified