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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 101–110 of 267 papers

TitleStatusHype
Hierarchical Models: Intrinsic Separability in High Dimensions—0
Feature Decoupling in Self-supervised Representation Learning for Open Set Recognition—0
Familiarity-Based Open-Set Recognition Under Adversarial Attacks—0
Collective Decision of One-vs-Rest Networks for Open Set Recognition—0
Hyperbolic Dual Feature Augmentation for Open-Environment—0
ID-Conditioned Auto-Encoder for Unsupervised Anomaly Detection—0
Face Detection on Surveillance Images—0
Individual common dolphin identification via metric embedding learning—0
Informed Decision-Making through Advancements in Open Set Recognition and Unknown Sample Detection—0
Collective decision for open set recognition—0
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