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 1941–1950 of 4856 papers

Title	Date	Tasks	Status	Hype
BiGSeT: Binary Mask-Guided Separation Training for DNN-based Hyperspectral Anomaly Detection	Jul 14, 2023	Anomaly Detection	CodeCode Available	0
Unsupervised Learning of Distributional Properties can Supplement Human Labeling and Increase Active Learning Efficiency in Anomaly Detection	Jul 13, 2023	Active LearningAnomaly Detection	—Unverified	0
DSV: An Alignment Validation Loss for Self-supervised Outlier Model Selection	Jul 13, 2023	Anomaly DetectionData Augmentation	CodeCode Available	0
Weakly supervised marine animal detection from remote sensing images using vector-quantized variational autoencoder	Jul 13, 2023	Anomaly DetectionAnomaly Localization	—Unverified	0
Neuro-symbolic Empowered Denoising Diffusion Probabilistic Models for Real-time Anomaly Detection in Industry 4.0	Jul 13, 2023	Anomaly DetectionDenoising	—Unverified	0
Personalized Anomaly Detection in PPG Data using Representation Learning and Biometric Identification	Jul 12, 2023	Anomaly DetectionPhotoplethysmography (PPG)	—Unverified	0
Visualization for Multivariate Gaussian Anomaly Detection in Images	Jul 12, 2023	Anomaly Detection	—Unverified	0
PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation	Jul 11, 2023	Anomaly ClassificationAnomaly Detection	CodeCode Available	1
A Comprehensive Survey of Deep Transfer Learning for Anomaly Detection in Industrial Time Series: Methods, Applications, and Directions	Jul 11, 2023	Anomaly DetectionDeep Learning	—Unverified	0
Temporal Graphs Anomaly Emergence Detection: Benchmarking For Social Media Interactions	Jul 11, 2023	Anomaly DetectionBenchmarking	—Unverified	0

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