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Model adaptation: Historical contrastive learning for unsupervised domain adaptation without source data
Unsupervised domain adaptation aims to align a labeled source domain and an unlabeled
target domain, but it requires to access the source data which often raises concerns in data …
target domain, but it requires to access the source data which often raises concerns in data …
Do we really need to access the source data? source hypothesis transfer for unsupervised domain adaptation
Unsupervised domain adaptation (UDA) aims to leverage the knowledge learned from a
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …
labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA …