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 4081–4090 of 4856 papers

Title	Date	Tasks	Status
AEGR: A simple approach to gradient reversal in autoencoders for network anomaly detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified
Data Augmentation by AutoEncoders for Unsupervised Anomaly Detection	Dec 21, 2019	Anomaly DetectionData Augmentation	—Unverified
NFAD: Fixing anomaly detection using normalizing flows	Dec 19, 2019	Anomaly DetectionBayesian Inference	CodeCode Available
Unsupervised Anomaly Detection in Stream Data with Online Evolving Spiking Neural Networks	Dec 18, 2019	Anomaly DetectionTime Series Analysis	—Unverified
Probabilistic Software Modeling: A Data-driven Paradigm for Software Analysis	Dec 17, 2019	Anomaly Detection	—Unverified
An Unsupervised Framework for Anomaly Detection in a Water Treatment System.	Dec 16, 2019	Anomaly DetectionDecoder	CodeCode Available
MimicGAN: Robust Projection onto Image Manifolds with Corruption Mimicking	Dec 16, 2019	Adversarial DefenseAnomaly Detection	—Unverified
TopoAct: Visually Exploring the Shape of Activations in Deep Learning	Dec 13, 2019	Anomaly DetectionDeep Learning	CodeCode Available
Changes to the extreme and erratic behaviour of cryptocurrencies during COVID-19	Dec 12, 2019	Anomaly DetectionTime Series	—Unverified
Enabling Machine Learning Across Heterogeneous Sensor Networks with Graph Autoencoders	Dec 12, 2019	Activity RecognitionAnomaly Detection	—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