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Found 3 papers, 1 papers with code
Learnable Data Augmentation for One-Shot Unsupervised Domain Adaptation
This paper presents a classification framework based on learnable data augmentation to tackle the One-Shot Unsupervised Domain Adaptation (OS-UDA) problem.
Data Augmentation
One-shot Unsupervised Domain Adaptation
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Target-driven One-Shot Unsupervised Domain Adaptation
Unlike existing methods, our augmentation module allows for strong transformations of the source samples, and the style of the single target sample available is exploited to guide the augmentation by ensuring perceptual similarity.
One-shot Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning
We formalize OZSL as the problem of recognizing seen and unseen classes (as in GZSL) while also rejecting instances from unknown categories, for which neither visual data nor class embeddings are provided.
