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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 51–100 of 155 papers

TitleStatusHype
A Neural Network-Based On-device Learning Anomaly Detector for Edge Devices—0
Anomaly Detection by Context Contrasting—0
Anomaly Detection for an E-commerce Pricing System—0
Anomaly Detection in Electrocardiograms: Advancing Clinical Diagnosis Through Self-Supervised Learning—0
Anomaly Detection in File Fragment Classification of Image File Formats—0
Anomaly Detection with Domain Adaptation—0
Anomaly Detection with Inexact Labels—0
AnoNet: Weakly Supervised Anomaly Detection in Textured Surfaces—0
AnoRand: A Semi Supervised Deep Learning Anomaly Detection Method by Random Labeling—0
A Self-Reasoning Framework for Anomaly Detection Using Video-Level Labels—0
Autoencoding Binary Classifiers for Supervised Anomaly Detection—0
Automated Processing of eXplainable Artificial Intelligence Outputs in Deep Learning Models for Fault Diagnostics of Large Infrastructures—0
BadSAD: Clean-Label Backdoor Attacks against Deep Semi-Supervised Anomaly Detection—0
Brain Tumor Anomaly Detection via Latent Regularized Adversarial Network—0
Bridging Unsupervised and Semi-Supervised Anomaly Detection: A Theoretically-Grounded and Practical Framework with Synthetic Anomalies—0
CLAWS: Clustering Assisted Weakly Supervised Learning with Normalcy Suppression for Anomalous Event Detection—0
Cleaning Label Noise with Clusters for Minimally Supervised Anomaly Detection—0
CL-Flow:Strengthening the Normalizing Flows by Contrastive Learning for Better Anomaly Detection—0
Clustering Aided Weakly Supervised Training to Detect Anomalous Events in Surveillance Videos—0
Deep Anomaly Detection and Search via Reinforcement Learning—0
Deep evolving semi-supervised anomaly detection—0
Deep Multi-Task Learning for Anomalous Driving Detection Using CAN Bus Scalar Sensor Data—0
Deep Semi-Supervised Anomaly Detection for Finding Fraud in the Futures Market—0
Directional anomaly detection—0
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection—0
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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