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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 221–230 of 267 papers

TitleStatusHype
Learning Unknowns from Unknowns: Diversified Negative Prototypes Generator for Few-Shot Open-Set RecognitionCode0
Towards Novel Target Discovery Through Open-Set Domain AdaptationCode0
Deep Active Learning via Open Set RecognitionCode0
Synthetic Non-stationary Data Streams for Recognition of the UnknownCode0
Open-Set Semi-Supervised Learning for Long-Tailed Medical DatasetsCode0
Mitigating Label Noise using Prompt-Based Hyperbolic Meta-Learning in Open-Set Domain GeneralizationCode0
MMF: A loss extension for feature learning in open set recognitionCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
3DOS: Towards 3D Open Set Learning -- Benchmarking and Understanding Semantic Novelty Detection on Point CloudsCode0
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