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 601–610 of 4856 papers

Title	Date	Tasks	Status	Hype
CARLA: Self-supervised Contrastive Representation Learning for Time Series Anomaly Detection	Aug 18, 2023	Anomaly DetectionContrastive Learning	CodeCode Available	1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels	Sep 25, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	1
Anomaly Detection in IR Images of PV Modules using Supervised Contrastive Learning	Dec 6, 2021	Anomaly DetectionContrastive Learning	CodeCode Available	1
CAT: Beyond Efficient Transformer for Content-Aware Anomaly Detection in Event Sequences	Aug 14, 2022	Anomaly DetectionDecoder	CodeCode Available	1
DFR: Deep Feature Reconstruction for Unsupervised Anomaly Segmentation	Dec 13, 2020	Anomaly DetectionAnomaly Segmentation	CodeCode Available	1
Can LLMs Understand Time Series Anomalies?	Oct 7, 2024	Anomaly DetectionTime Series	CodeCode Available	1
Diffusion Models for Medical Anomaly Detection	Mar 8, 2022	Anomaly DetectionDenoising	CodeCode Available	1
Anomaly Detection of Wind Turbine Time Series using Variational Recurrent Autoencoders	Dec 5, 2021	Anomaly DetectionTime Series	CodeCode Available	1
Camouflaged Object Detection	Jun 1, 2020	Anomaly DetectionCamouflaged Object Segmentation	CodeCode Available	1
Can Multimodal LLMs Perform Time Series Anomaly Detection?	Feb 25, 2025	Anomaly DetectionIrregular Time Series	CodeCode Available	1

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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	GLASS	Detection AUROC	99.9	—	Unverified
5	UniNet	Detection AUROC	99.9	—	Unverified
6	INP-Fomer ViT-L (model-unified multi-class)	Detection AUROC	99.8	—	Unverified
7	DDAD	Detection AUROC	99.8	—	Unverified
8	EfficientAD (early stopping)	Detection AUROC	99.8	—	Unverified
9	PBAS	Detection AUROC	99.8	—	Unverified
10	HETMM	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	DDAD	Detection AUROC	98.9	—	Unverified
5	Dinomaly ViT-L (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
6	INP-Former ViT-B (model-unified multi-class)	Detection AUROC	98.9	—	Unverified
7	DiffusionAD	Detection AUROC	98.8	—	Unverified
8	GLASS	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