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Found 2 papers, 2 papers with code
Gradual Domain Adaptation via Normalizing Flows
In previous work, it is assumed that the number of intermediate domains is large and the distance between adjacent domains is small; hence, the gradual domain adaptation algorithm, involving self-training with unlabeled datasets, is applicable.
Cost-effective Framework for Gradual Domain Adaptation with Multifidelity
Practically, the cost of samples in intermediate domains will vary, and it is natural to consider that the closer an intermediate domain is to the target domain, the higher the cost of obtaining samples from the intermediate domain is.
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