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Open Set Learning

Traditional supervised learning aims to train a classifier in the closed-set world, where training and test samples share the same label space. Open set learning (OSL) is a more challenging and realistic setting, where there exist test samples from the classes that are unseen during training. Open set recognition (OSR) is the sub-task of detecting test samples which do not come from the training.

Papers

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Showing 131–140 of 267 papers

TitleStatusHype
OpenAPMax: Abnormal Patterns-based Model for Real-World Alzheimer's Disease Diagnosis—0
OpenClinicalAI: An Open and Dynamic Model for Alzheimer's Disease Diagnosis—0
OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection—0
Open-Set RF Fingerprinting via Improved Prototype Learning—0
Learning for Transductive Threshold Calibration in Open-World Recognition—0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition—0
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification—0
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
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