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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 101–125 of 267 papers

TitleStatusHype
Hierarchical Models: Intrinsic Separability in High Dimensions—0
FedPD: Federated Open Set Recognition with Parameter Disentanglement—0
Feature-Space Semantic Invariance: Enhanced OOD Detection for Open-Set Domain Generalization—0
A novel network training approach for open set image recognition—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
On the link between generative semi-supervised learning and generative open-set recognition—0
Individual common dolphin identification via metric embedding learning—0
Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Familiarity-Based Open-Set Recognition Under Adversarial Attacks—0
Open-set learning with augmented categories by exploiting unlabelled data—0
Denoising Autoencoders for Overgeneralization in Neural Networks—0
Collective Decision of One-vs-Rest Networks for Open Set Recognition—0
Knowledge is Never Enough: Towards Web Aided Deep Open World Recognition—0
Face Detection on Surveillance Images—0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Collective decision for open set recognition—0
Comparison of Embedded Spaces for Deep Learning Classification—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
LORD: Leveraging Open-Set Recognition with Unknown Data—0
An Entropy-Awareness Meta-Learning Method for SAR Open-Set ATR—0
Evaluation of Various Open-Set Medical Imaging Tasks with Deep Neural Networks—0
Evaluating Uncertainty Calibration for Open-Set Recognition—0
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