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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 76–100 of 267 papers

TitleStatusHype
Deep Compact Polyhedral Conic Classifier for Open and Closed Set Recognition—0
A Survey on Open Set Recognition—0
An In-Depth Study on Open-Set Camera Model Identification—0
A Survey on Open-Set Image Recognition—0
Data-Driven Hierarchical Open Set Recognition—0
Alignment Based Matching Networks for One-Shot Classification and Open-Set Recognition—0
Implicit supervision for fault detection and segmentation of emerging fault types with Deep Variational Autoencoders—0
Know Yourself Better: Diverse Discriminative Feature Learning Improves Open Set Recognition—0
Latent Space Energy-based Model for Fine-grained Open Set Recognition—0
Adversarial Robustness: Softmax versus Openmax—0
FSOS-AMC: Few-Shot Open-Set Learning for Automatic Modulation Classification—0
Informed Mixing -- Improving Open Set Recognition via Attribution-based Augmentation—0
From Coarse to Fine-Grained Open-Set Recognition—0
Few-shot Open-set Recognition Using Background as Unknowns—0
Are Out-of-Distribution Detection Methods Effective on Large-Scale Datasets?—0
Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection—0
FedPD: Federated Open Set Recognition with Parameter Disentanglement—0
Gallery-Aware Uncertainty Estimation For Open-Set Face Recognition—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
Generalizing Cross Entropy Loss with a Beta Proper Composite Loss: An Improved Loss Function for Open Set Recognition—0
Generative-Discriminative Feature Representations for Open-Set Recognition—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Familiarity-Based Open-Set Recognition Under Adversarial Attacks—0
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