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 1611–1620 of 4856 papers

Title	Date	Tasks	Status	Score
Building and Interpreting Deep Similarity Models	Mar 11, 2020	Anomaly DetectionClustering	CodeCode Available	5
Anomaly Detection in High Dimensional Data	Aug 12, 2019	Anomaly DetectionFeature Engineering	CodeCode Available	5
Enhanced anomaly detection in well log data through the application of ensemble GANs	Nov 29, 2024	Anomaly Detection	CodeCode Available	5
Enhancing Wrist Fracture Detection with YOLO	Jul 17, 2024	Anomaly DetectionFracture detection	CodeCode Available	5
Expected Similarity Estimation for Large-Scale Batch and Streaming Anomaly Detection	Jan 25, 2016	Anomaly DetectionIncremental Learning	CodeCode Available	5
Feature Attenuation of Defective Representation Can Resolve Incomplete Masking on Anomaly Detection	Jul 5, 2024	Anomaly DetectionEdge-computing	CodeCode Available	5
Effective and Efficient Representation Learning for Flight Trajectories	Dec 21, 2024	Anomaly DetectionRepresentation Learning	CodeCode Available	5
Effect of Deep Transfer and Multi task Learning on Sperm Abnormality Detection	Nov 21, 2021	Anomaly DetectionMulti-Task Learning	CodeCode Available	5
BRUNO: A Deep Recurrent Model for Exchangeable Data	Feb 21, 2018	Anomaly DetectionBayesian Inference	CodeCode Available	5
AIDA: Analytic Isolation and Distance-based Anomaly Detection Algorithm	Dec 5, 2022	Anomaly Detection	CodeCode Available	5

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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