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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 11–20 of 267 papers

TitleStatusHype
Conditional Gaussian Distribution Learning for Open Set RecognitionCode1
COOOL: Challenge Of Out-Of-Label A Novel Benchmark for Autonomous DrivingCode1
Exploring Diverse Representations for Open Set RecognitionCode1
Conditional Variational Capsule Network for Open Set RecognitionCode1
Counterfactual Zero-Shot and Open-Set Visual RecognitionCode1
A Unified Survey on Anomaly, Novelty, Open-Set, and Out-of-Distribution Detection: Solutions and Future ChallengesCode1
Adversarial Motorial Prototype Framework for Open Set RecognitionCode1
A Unified Benchmark for the Unknown Detection Capability of Deep Neural NetworksCode1
Adversarial Reciprocal Points Learning for Open Set RecognitionCode1
DenseHybrid: Hybrid Anomaly Detection for Dense Open-set RecognitionCode1
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