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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 91–100 of 267 papers

TitleStatusHype
Uncovering the Hidden Dynamics of Video Self-supervised Learning under Distribution ShiftsCode1
Few-Shot Open-Set Learning for On-Device Customization of KeyWord Spotting SystemsCode1
In or Out? Fixing ImageNet Out-of-Distribution Detection EvaluationCode1
Learning for Transductive Threshold Calibration in Open-World Recognition—0
torchosr -- a PyTorch extension package for Open Set Recognition models evaluation in PythonCode1
MDENet: Multi-modal Dual-embedding Networks for Malware Open-set Recognition—0
CNS-Net: Conservative Novelty Synthesizing Network for Malware Recognition in an Open-set ScenarioCode0
Glocal Energy-based Learning for Few-Shot Open-Set RecognitionCode1
OpenMix+: Revisiting Data Augmentation for Open Set RecognitionCode0
Uncertainty-inspired Open Set Learning for Retinal Anomaly Identification—0
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