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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 151–160 of 267 papers

TitleStatusHype
Video Anomaly Detection via Spatio-Temporal Pseudo-Anomaly Generation : A Unified Approach—0
Visual and Semantic Prototypes-Jointly Guided CNN for Generalized Zero-shot Learning—0
Vocabulary-informed Extreme Value Learning—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
An In-Depth Study on Open-Set Camera Model Identification—0
Zero-X: A Blockchain-Enabled Open-Set Federated Learning Framework for Zero-Day Attack Detection in IoV—0
Comparison of Embedded Spaces for Deep Learning Classification—0
Open Set Recognition for Random Forest—0
Advanced Vision Transformers and Open-Set Learning for Robust Mosquito Classification: A Novel Approach to Entomological Studies—0
Adversarial Robustness: Softmax versus Openmax—0
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