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 831–840 of 4856 papers

Title	Date	Tasks	Status	Hype	Score
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study	Nov 5, 2023	Anomaly Detection	CodeCode Available	1	5
A Background-Agnostic Framework with Adversarial Training for Abnormal Event Detection in Video	Aug 27, 2020	Abnormal Event Detection In VideoAnomaly Detection	CodeCode Available	1	5
Invariant Anomaly Detection under Distribution Shifts: A Causal Perspective	Dec 21, 2023	Anomaly DetectionCausal Inference	CodeCode Available	1	5
Interpreting Rate-Distortion of Variational Autoencoder and Using Model Uncertainty for Anomaly Detection	May 5, 2020	Anomaly DetectionBIG-bench Machine Learning	CodeCode Available	1	5
Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection	Oct 14, 2024	Anomaly DetectionPrompt Learning	CodeCode Available	1	5
First-shot anomaly sound detection for machine condition monitoring: A domain generalization baseline	Mar 1, 2023	Anomaly DetectionDomain Generalization	CodeCode Available	1	5
Interpretable, Multidimensional, Multimodal Anomaly Detection with Negative Sampling for Detection of Device Failure	Jul 12, 2020	Anomaly DetectionAttribute	CodeCode Available	1	5
FlexFringe: Modeling Software Behavior by Learning Probabilistic Automata	Mar 28, 2022	Anomaly Detection	CodeCode Available	1	5
Inter-Realization Channels: Unsupervised Anomaly Detection Beyond One-Class Classification	Jan 1, 2023	Anomaly DetectionOne-Class Classification	CodeCode Available	1	5
Asymmetric Student-Teacher Networks for Industrial Anomaly Detection	Oct 14, 2022	3D Anomaly DetectionAnomaly Detection	CodeCode Available	1	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