Semi-supervised Anomaly Detection
28 papers with code • 1 benchmarks • 2 datasets
Libraries
Use these libraries to find Semi-supervised Anomaly Detection models and implementationsMost implemented papers
Unseen Anomaly Detection on Networks via Multi-Hypersphere Learning
Network anomaly detection is a crucial task since a few anomalies can cause huge losses.
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.
SAD: Semi-Supervised Anomaly Detection on Dynamic Graphs
Anomaly detection aims to distinguish abnormal instances that deviate significantly from the majority of benign ones.
On Diffusion Modeling for Anomaly Detection
By simplifying DDPM in application to anomaly detection, we are naturally led to an alternative approach called Diffusion Time Estimation (DTE).
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.
ImbSAM: A Closer Look at Sharpness-Aware Minimization in Class-Imbalanced Recognition
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).
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.