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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 101–110 of 267 papers

TitleStatusHype
Simple Domain Generalization Methods are Strong Baselines for Open Domain GeneralizationCode0
On the link between generative semi-supervised learning and generative open-set recognition—0
Progressive Open Space Expansion for Open-Set Model AttributionCode1
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios—0
Open-World Object Detection via Discriminative Class Prototype Learning—0
Zero-Knowledge Zero-Shot Learning for Novel Visual Category Discovery—0
The Devil is in the Wrongly-classified Samples: Towards Unified Open-set RecognitionCode1
Improving Open-Set Semi-Supervised Learning with Self-SupervisionCode0
Contracting Skeletal Kinematics for Human-Related Video Anomaly DetectionCode0
Open-Set Likelihood Maximization for Few-Shot LearningCode1
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