• Corpus ID: 246867139

Learning to Generalize across Domains on Single Test Samples

@article{Xiao2022LearningTG,
  title={Learning to Generalize across Domains on Single Test Samples},
  author={Zehao Xiao and Xiantong Zhen and Ling Shao and Cees G. M. Snoek},
  journal={ArXiv},
  year={2022},
  volume={abs/2202.08045}
}
We strive to learn a model from a set of source domains that generalizes well to unseen target domains. The main challenge in such a domain generalization scenario is the unavailability of any target domain data during training, resulting in the learned model not being explicitly adapted to the unseen target domains. We propose learning to generalize across domains on single test samples. We leverage a meta-learning paradigm to learn our model to acquire the ability of adaptation with single… 
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Hierarchical Variational Memory for Few-shot Learning Across Domains
TLDR
Learning to weigh prototypes in a data-driven way, which further improves generalization performance and the effectiveness of hierarchical variational memory in handling both the domain shift and few-shot learning problems is proposed.
FEW-SHOT LEARNING ACROSS DOMAINS
  • 2022

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