Learning to adapt (meta-learning for domain shift)
Learning to adapt, or meta-learning for domain shift, refers to methods in machine learning that enable models to generalize across varying domains.
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Learning to adapt, or meta-learning for domain shift, refers to methods in machine learning that enable models to generalize across varying domains.