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 381–390 of 4856 papers

Title	Date	Tasks	Status	Hype
NuTime: Numerically Multi-Scaled Embedding for Large-Scale Time-Series Pretraining	Oct 11, 2023	Anomaly DetectionFew-Shot Learning	CodeCode Available	1
IPMix: Label-Preserving Data Augmentation Method for Training Robust Classifiers	Oct 7, 2023	Anomaly DetectionData Augmentation	CodeCode Available	1
Twin Graph-based Anomaly Detection via Attentive Multi-Modal Learning for Microservice System	Oct 7, 2023	Anomaly DetectionScheduling	CodeCode Available	1
T-Rep: Representation Learning for Time Series using Time-Embeddings	Oct 6, 2023	Anomaly DetectionRepresentation Learning	CodeCode Available	1
Delving into CLIP latent space for Video Anomaly Recognition	Oct 4, 2023	Anomaly DetectionMultiple Instance Learning	CodeCode Available	1
Unravel Anomalies: An End-to-end Seasonal-Trend Decomposition Approach for Time Series Anomaly Detection	Sep 30, 2023	Anomaly DetectionTime Series	CodeCode Available	1
ADGym: Design Choices for Deep Anomaly Detection	Sep 27, 2023	Anomaly DetectionCloud Computing	CodeCode Available	1
Detach-ROCKET: Sequential feature selection for time series classification with random convolutional kernels	Sep 25, 2023	Anomaly DetectionComputational Efficiency	CodeCode Available	1
LogGPT: Log Anomaly Detection via GPT	Sep 25, 2023	Anomaly DetectionLanguage Modeling	CodeCode Available	1
Real3D-AD: A Dataset of Point Cloud Anomaly Detection	Sep 23, 2023	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1

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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