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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 251–267 of 267 papers

TitleStatusHype
Open-Set Plankton Recognition—0
Open Set Recognition For Music Genre Classification—0
Open-Set Recognition of Breast Cancer Treatments—0
Open-Set Recognition of Novel Species in Biodiversity Monitoring—0
Open Set Recognition Through Deep Neural Network Uncertainty: Does Out-of-Distribution Detection Require Generative Classifiers?—0
Open-Set Recognition Using Intra-Class Splitting—0
Open-set Recognition via Augmentation-based Similarity Learning—0
Open Set Recognition with Conditional Probabilistic Generative Models—0
Open-Set Recognition with Gaussian Mixture Variational Autoencoders—0
Open-Set Recognition with Gradient-Based Representations—0
Open-Set RF Fingerprinting via Improved Prototype Learning—0
Open-Set Video-based Facial Expression Recognition with Human Expression-sensitive Prompting—0
Open Set Wireless Transmitter Authorization: Deep Learning Approaches and Dataset Considerations—0
OpenSlot: Mixed Open-Set Recognition with Object-Centric Learning—0
Open-World Object Detection via Discriminative Class Prototype Learning—0
Orthogonal-Coding-Based Feature Generation for Transductive Open-Set Recognition via Dual-Space Consistent Sampling—0
Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey—0
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