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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