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 3681–3690 of 4856 papers

Title	Date	Tasks	Status	Hype
Using Channel State Information for Physical Tamper Attack Detection in OFDM Systems: A Deep Learning Approach	Nov 6, 2020	Anomaly DetectionSemi-supervised Anomaly Detection	—Unverified	0
UAV-AdNet: Unsupervised Anomaly Detection using Deep Neural Networks for Aerial Surveillance	Nov 5, 2020	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Anomalous Sound Detection as a Simple Binary Classification Problem with Careful Selection of Proxy Outlier Examples	Nov 5, 2020	Anomaly DetectionBinary Classification	CodeCode Available	1
Robust Unsupervised Video Anomaly Detection by Multi-Path Frame Prediction	Nov 5, 2020	Anomaly DetectionPrediction	—Unverified	0
Learning and Evaluating Representations for Deep One-class Classification	Nov 4, 2020	Anomaly DetectionClassification	CodeCode Available	1
Video Generative Adversarial Networks: A Review	Nov 4, 2020	Anomaly Detection	—Unverified	0
Detecting Backdoors in Neural Networks Using Novel Feature-Based Anomaly Detection	Nov 4, 2020	Anomaly DetectionData Augmentation	—Unverified	0
Autoencoding Features for Aviation Machine Learning Problems	Nov 3, 2020	Anomaly DetectionBIG-bench Machine Learning	—Unverified	0
Unsupervised Anomaly Detection in Parole Hearings using Language Models	Nov 1, 2020	Anomaly DetectionUnsupervised Anomaly Detection	—Unverified	0
Comprehensible Counterfactual Explanation on Kolmogorov-Smirnov Test	Nov 1, 2020	Anomaly DetectionAstronomy	—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