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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 51–60 of 155 papers

TitleStatusHype
An AI System for Continuous Knee Osteoarthritis Severity Grading Using Self-Supervised Anomaly Detection with Limited DataCode0
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization—0
Weakly-supervised anomaly detection for multimodal data distributions—0
Pancreatic Tumor Segmentation as Anomaly Detection in CT Images Using Denoising Diffusion Models—0
Anomaly Detection by Context Contrasting—0
Deep Positive-Unlabeled Anomaly Detection for Contaminated Unlabeled DataCode0
Qsco: A Quantum Scoring Module for Open-set Supervised Anomaly Detection—0
Semi-supervised Anomaly Detection via Adaptive Reinforcement Learning-Enabled Method with Causal Inference for Sensor SignalsCode0
MAPL: Memory Augmentation and Pseudo-Labeling for Semi-Supervised Anomaly DetectionCode0
IgCONDA-PET: Weakly-Supervised PET Anomaly Detection using Implicitly-Guided Attention-Conditional Counterfactual Diffusion Modeling -- a Multi-Center, Multi-Cancer, and Multi-Tracer StudyCode0
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