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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 81–90 of 267 papers

TitleStatusHype
Non-Exhaustive Learning Using Gaussian Mixture Generative Adversarial NetworksCode0
FedOS: using open-set learning to stabilize training in federated learningCode0
AP18-OLR Challenge: Three Tasks and Their BaselinesCode0
Multi-Attribute Open Set RecognitionCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Exploring the Open World Using Incremental Extreme Value MachinesCode0
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
MMF: A loss extension for feature learning in open set recognitionCode0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
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