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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 221–230 of 267 papers

TitleStatusHype
Deep Learning and Open Set Malware Classification: A Survey—0
Class Anchor Clustering: a Loss for Distance-based Open Set RecognitionCode1
Hybrid Models for Open Set Recognition—0
Conditional Gaussian Distribution Learning for Open Set RecognitionCode1
Hierarchical Models: Intrinsic Separability in High Dimensions—0
An Open-set Recognition and Few-Shot Learning Dataset for Audio Event Classification in Domestic EnvironmentsCode0
Deep Learning Approaches for Open Set Wireless Transmitter Authorization—0
Open-set learning with augmented categories by exploiting unlabelled data—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
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