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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 191–200 of 267 papers

TitleStatusHype
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition—0
Measuring Human Perception to Improve Open Set Recognition—0
MENTOR: Human Perception-Guided Pretraining for Increased Generalization—0
MetaMax: Improved Open-Set Deep Neural Networks via Weibull Calibration—0
More Information Supervised Probabilistic Deep Face Embedding Learning—0
Rectifying Open-set Object Detection: A Taxonomy, Practical Applications, and Proper Evaluation—0
One-vs-Rest Network-based Deep Probability Model for Open Set Recognition—0
OOD Augmentation May Be at Odds with Open-Set Recognition—0
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
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