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 151–160 of 4856 papers

Title	Date	Tasks	Status	Hype
Anomaly Transformer: Time Series Anomaly Detection with Association Discrepancy	Oct 6, 2021	Anomaly DetectionTime Series	CodeCode Available	2
Tribuo: Machine Learning with Provenance in Java	Oct 6, 2021	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	2
Towards Total Recall in Industrial Anomaly Detection	Jun 15, 2021	3D Anomaly DetectionAnomaly Classification	CodeCode Available	2
Is Space-Time Attention All You Need for Video Understanding?	Feb 9, 2021	Action ClassificationAction Recognition	CodeCode Available	2
TadGAN: Time Series Anomaly Detection Using Generative Adversarial Networks	Sep 16, 2020	Anomaly DetectionBenchmarking	CodeCode Available	2
Self-Supervised Log Parsing	Mar 17, 2020	Anomaly DetectionFault Detection	CodeCode Available	2
Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic Thresholding	Feb 13, 2018	Anomaly Detection	CodeCode Available	2
Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal KPIs in Web Applications	Feb 12, 2018	Anomaly DetectionUnsupervised Anomaly Detection	CodeCode Available	2
A Multimodal Anomaly Detector for Robot-Assisted Feeding Using an LSTM-based Variational Autoencoder	Nov 2, 2017	Anomaly Detection	CodeCode Available	2
LSTM-based Encoder-Decoder for Multi-sensor Anomaly Detection	Jul 1, 2016	Anomaly DetectionDecoder	CodeCode Available	2

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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