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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 61–70 of 267 papers

TitleStatusHype
Disentangled representations of microscopy imagesCode0
SASep: Saliency-Aware Structured Separation of Geometry and Feature for Open Set Learning on Point CloudsCode0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
Open-Set Semi-Supervised Learning for Long-Tailed Medical DatasetsCode0
Synthetic Non-stationary Data Streams for Recognition of the UnknownCode0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
Structure-based Anomaly Detection and Clustering—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Open-Set Plankton Recognition—0
SphOR: A Representation Learning Perspective on Open-set Recognition for Identifying Unknown Classes in Deep Learning ModelsCode0
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