Semi-supervised Anomaly Detection
28 papers with code • 1 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Semi-supervised Anomaly Detection
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Libraries
Use these libraries to find Semi-supervised Anomaly Detection models and implementations
MVTec LOCO AD
UBI-Fights
Latest papers
NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
While AD is typically treated as an unsupervised learning task due to the high cost of label annotation, it is more practical to assume access to a small set of labeled anomaly samples from domain experts, as is the case for semi-supervised anomaly detection.
Label-based Graph Augmentation with Metapath for Graph Anomaly Detection
missinghwan/MSAD
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To further efficiently exploit context information from metapath-based anomaly subgraph, we present a new framework, Metapath-based Graph Anomaly Detection (MGAD), incorporating GCN layers in both the dual-encoders and decoders to efficiently propagate context information between abnormal and normal nodes.
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
cool-xuan/imbalanced_sam
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ICCV 2023
To overcome this bottleneck, we leverage class priors to restrict the generalization scope of the class-agnostic SAM and propose a class-aware smoothness optimization algorithm named Imbalanced-SAM (ImbSAM).
RoSAS: Deep Semi-Supervised Anomaly Detection with Contamination-Resilient Continuous Supervision
xuhongzuo/rosas
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anomaly contamination.
AnoOnly: Semi-Supervised Anomaly Detection with the Only Loss on Anomalies
Unlike existing SSAD methods that resort to strict loss supervision, AnoOnly suspends it and introduces a form of weak supervision for normal data.
On Diffusion Modeling for Anomaly Detection
vicliv/dte
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By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
d10andy/sad
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Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
EfficientAD: Accurate Visual Anomaly Detection at Millisecond-Level Latencies
We train a student network to predict the extracted features of normal, i. e., anomaly-free training images.
Leveraging Contaminated Datasets to Learn Clean-Data Distribution with Purified Generative Adversarial Networks
tbw162/purigan
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When training on such datasets, existing GANs will learn a mixture distribution of desired and contaminated instances, rather than the desired distribution of desired data only (target distribution).
R2-AD2: Detecting Anomalies by Analysing the Raw Gradient
Neural networks follow a gradient-based learning scheme, adapting their mapping parameters by back-propagating the output loss.
