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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 91–100 of 267 papers

TitleStatusHype
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
LEGO-Learn: Label-Efficient Graph Open-Set LearningCode0
Cross-Rejective Open-Set SAR Image RegistrationCode0
Classification-Reconstruction Learning for Open-Set RecognitionCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
Deep Active Learning via Open Set RecognitionCode0
Dynamic Against Dynamic: An Open-set Self-learning FrameworkCode0
Invisible Backdoor Attack with Dynamic Triggers against Person Re-identificationCode0
Domain Consensus Clustering for Universal Domain AdaptationCode0
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