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 4401–4410 of 4856 papers

Title	Date	Tasks	Status	Hype
Autoencoders and Generative Adversarial Networks for Imbalanced Sequence Classification	Jan 8, 2019	Anomaly DetectionClassification	CodeCode Available	0
PyOD: A Python Toolbox for Scalable Outlier Detection	Jan 6, 2019	Anomaly DetectionOutlier Detection	CodeCode Available	1
Natively Interpretable Machine Learning and Artificial Intelligence: Preliminary Results and Future Directions	Jan 2, 2019	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Feedforward Neural Network for Time Series Anomaly Detection	Dec 20, 2018	Anomaly DetectionCPU	—Unverified	0
An Evaluation of Methods for Real-Time Anomaly Detection using Force Measurements from the Turning Process	Dec 20, 2018	Anomaly Detection	CodeCode Available	0
DeepAnT: A Deep Learning Approach for Unsupervised Anomaly Detection in Time Series	Dec 19, 2018	Anomaly DetectionTime Series	CodeCode Available	0
Correlated Anomaly Detection from Large Streaming Data	Dec 19, 2018	Anomaly DetectionEvent Detection	—Unverified	0
Video Trajectory Classification and Anomaly Detection Using Hybrid CNN-VAE	Dec 18, 2018	Anomaly DetectionClassification	CodeCode Available	0
Anomaly Detection and Interpretation using Multimodal Autoencoder and Sparse Optimization	Dec 18, 2018	Anomaly Detection	—Unverified	0
Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention	Dec 17, 2018	Anomaly DetectionDeep Attention	—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	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