Most domain adaptation methods focus on single-source-single-target adaptation setting. Multi-target domain adaptation is a powerful extension in which a single classifier is learned for multiple unlabeled target domains. To build a multi-target classifier, it is crucial to effectively aggregate features from the labeled source and different unlabeled target domains. Towards this, recently introduced Domain-aware Curriculum Graph Co-Teaching (D-CGCT) exploits dual classifier head, one of which is based on the graph neural network. D-CGCT uses a sequential adaptation strategy that adapts one domain at a time starting from the target domains that are more similar to the source, assuming that the network finds it easier to adapt to such target domains. However, we argue that there is no easier domain or difficult domain in absolute sense and each domain can have samples showing different characteristics. Following this cue, we propose Reiterative D-CGCT (RD-CGCT) that obtains better adaptation performance by reiterating multiple times over each target domain, while keeping the total number of iterations as same. RD-CGCT further improves the adaptation performance by considering more source samples than training samples in the training minibatch. Proposed RD-CGCT significantly improves the performance over D-CGCT for Office-Home and Office31 datasets.
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