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Supervised Anomaly Detection

In the training set, the amount of abnormal samples is limited and significant fewer than normal samples, producing data distributions that lead to a naturally imbalanced learning problem.

Papers

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Showing 76–100 of 155 papers

TitleStatusHype
Meta-learning One-class Classifiers with Eigenvalue Solvers for Supervised Anomaly Detection—0
MKF-ADS: Multi-Knowledge Fusion Based Self-supervised Anomaly Detection System for Control Area Network—0
Neural Batch Sampling with Reinforcement Learning for Semi-Supervised Anomaly Detection—0
No Shifted Augmentations (NSA): compact distributions for robust self-supervised Anomaly Detection—0
No Shifted Augmentations (NSA): strong baselines for self-supervised Anomaly Detection—0
On the Impact of Object and Sub-component Level Segmentation Strategies for Supervised Anomaly Detection within X-ray Security Imagery—0
Open-Set Graph Anomaly Detection via Normal Structure Regularisation—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
Prototypical Residual Networks for Anomaly Detection and Localization—0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
RADE: Resource-Efficient Supervised Anomaly Detection Using Decision Tree-Based Ensemble Methods—0
A Radon-Nikodým Perspective on Anomaly Detection: Theory and Implications—0
Reconstruction Error-based Anomaly Detection with Few Outlying Examples—0
Robust Semi-Supervised Anomaly Detection via Adversarially Learned Continuous Noise Corruption—0
SAFE: Self-Supervised Anomaly Detection Framework for Intrusion Detection—0
Self-Supervised Anomaly Detection in Computer Vision and Beyond: A Survey and Outlook—0
Self-Supervised Anomaly Detection in the Wild: Favor Joint Embeddings Methods—0
Self-Supervised Anomaly Detection of Rogue Soil Moisture Sensors—0
SeMAnD: Self-Supervised Anomaly Detection in Multimodal Geospatial Datasets—0
Semi-supervised anomaly detection algorithm based on KL divergence (SAD-KL)—0
Semi-Supervised Anomaly Detection Based on Quadratic Multiform Separation—0
Semi-Supervised Anomaly Detection for the Determination of Vehicle Hijacking Tweets—0
Semi-supervised Anomaly Detection with Extremely Limited Labels in Dynamic Graphs—0
Semi-Supervised Health Index Monitoring with Feature Generation and Fusion—0
Semi-supervised learning via DQN for log anomaly detection—0
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