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 4581–4590 of 4856 papers

Title	Date	Tasks	Status
Online Topology Identification from Vector Autoregressive Time Series	Apr 3, 2019	Anomaly DetectionTime Series	CodeCode Available
DAD++: Improved Data-free Test Time Adversarial Defense	Sep 10, 2023	Adversarial DefenseAdversarial Robustness	CodeCode Available
Self-supervised vision-langage alignment of deep learning representations for bone X-rays analysis	May 14, 2024	Anomaly Detection	CodeCode Available
Deep State Inference: Toward Behavioral Model Inference of Black-box Software Systems	Jan 13, 2021	Anomaly DetectionChange Point Detection	CodeCode Available
On Periodicity Detection and Structural Periodic Similarity	Apr 21, 2005	Anomaly DetectionTime Series	CodeCode Available
Self-Taught Semi-Supervised Anomaly Detection on Upper Limb X-rays	Feb 19, 2021	Anomaly DetectionSelf-Supervised Anomaly Detection	CodeCode Available
Unsupervised real-time anomaly detection for streaming data	Jun 2, 2017	Anomaly DetectionTime Series	CodeCode Available
Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection	Mar 24, 2022	Anomaly DetectionMultiple Instance Learning	CodeCode Available
Anomaly Detection of Adversarial Examples using Class-conditional Generative Adversarial Networks	May 21, 2021	Anomaly Detectionimage-classification	CodeCode Available
on the effectiveness of generative adversarial network on anomaly detection	Dec 31, 2021	Anomaly DetectionGenerative Adversarial Network	CodeCode Available

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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