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ConDA: Continual Unsupervised Domain Adaptation
[article]
2021
arXiv
pre-print
Domain Adaptation (DA) techniques are important for overcoming the domain shift between the source domain used for training and the target domain where testing takes place. However, current DA methods assume that the entire target domain is available during adaptation, which may not hold in practice. This paper considers a more realistic scenario, where target data become available in smaller batches and adaptation on the entire target domain is not feasible. In our work, we introduce a new,
arXiv:2103.11056v2
fatcat:x362gfmdljg7tdfoodr3j5ugvu