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Contrastive Vicinal Space for Unsupervised Domain Adaptation
[article]
2022
arXiv
pre-print
Recent unsupervised domain adaptation methods have utilized vicinal space between the source and target domains. However, the equilibrium collapse of labels, a problem where the source labels are dominant over the target labels in the predictions of vicinal instances, has never been addressed. In this paper, we propose an instance-wise minimax strategy that minimizes the entropy of high uncertainty instances in the vicinal space to tackle the stated problem. We divide the vicinal space into two
arXiv:2111.13353v3
fatcat:slhnvarya5gdtox3gib5qdwgym