Self-Adaptation for Unsupervised Domain Adaptation
Xia Cui, Danushka Bollegala · 2019
Lack of labelled data in the target domain for training is a common problem in domain adaptation.To overcome this problem, we propose a novel unsupervised domain adaptation method that combines projection and self-training based approaches.Using the labelled data from the source domain, we first learn a projection that maximises the distance among the nearest neighbours with opposite labels in the source domain.Next, we project the source domain labelled data using the learnt projection and train a classifier for the target class prediction.We then use the trained classifier to predict pseudo labels for the target domain unlabelled data.Finally, we learn a projection for the target domain as we did for the source domain using the pseudo-labelled target domain data, where we maximise the distance between nearest neighbours having opposite pseudo labels.Experiments on a standard benchmark dataset for domain adaptation show that the proposed method consistently outperforms numerous baselines and returns competitive results comparable to that of SOTA including self-training, tri-training, and neural adaptations.