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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 151–175 of 267 papers

TitleStatusHype
Prototype-based Domain Generalization Framework for Subject-Independent Brain-Computer Interfaces—0
Recent Advances in Open Set Recognition: A Survey—0
Recent Advances in Zero-shot Recognition—0
Electromagnetic Scattering Kernel Guided Reciprocal Point Learning for SAR Open-Set Recognition—0
Recognition Awareness: An Application of Latent Cognizance to Open-Set Recognition—0
Recognize Any Surgical Object: Unleashing the Power of Weakly-Supervised Data—0
Recursive Counterfactual Deconfounding for Object Recognition—0
Representation learning with function call graph transformations for malware open set recognition—0
Rethinking Few-Shot Class-Incremental Learning with Open-Set Hypothesis in Hyperbolic Geometry—0
Revealing the Two Sides of Data Augmentation: An Asymmetric Distillation-based Win-Win Solution for Open-Set Recognition—0
Know Your Space: Inlier and Outlier Construction for Calibrating Medical OOD Detectors—0
ROG_PL: Robust Open-Set Graph Learning via Region-Based Prototype Learning—0
M-Tuning: Prompt Tuning with Mitigated Label Bias in Open-Set Scenarios—0
Self-supervised Detransformation Autoencoder for Representation Learning in Open Set Recognition—0
Semi-supervised Vocabulary-informed Learning—0
Solution for OOD-CV Workshop SSB Challenge 2024 (Open-Set Recognition Track)—0
Spatial-Temporal Attention Network for Open-Set Fine-Grained Image Recognition—0
Specialized Support Vector Machines for Open-set Recognition—0
Structure-based Anomaly Detection and Clustering—0
Subject-Independent Brain-Computer Interfaces with Open-Set Subject Recognition—0
Synthetic Unknown Class Learning for Learning Unknowns—0
Taking Class Imbalance Into Account in Open Set Recognition Evaluation—0
Teacher-Explorer-Student Learning: A Novel Learning Method for Open Set Recognition—0
Textbook Question Answering with Multi-modal Context Graph Understanding and Self-supervised Open-set Comprehension—0
The Importance of Metric Learning for Robotic Vision: Open Set Recognition and Active Learning—0
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