  In this paper we address multi-target domain adaptation (MTDA), where given
one labeled source dataset and multiple unlabeled target datasets that differ
in data distributions, the task is to learn a robust predictor for all the
target domains. We identify two key aspects that can help to alleviate multiple
domain-shifts in the MTDA: feature aggregation and curriculum learning. To this
end, we propose Curriculum Graph Co-Teaching (CGCT) that uses a dual classifier
head, with one of them being a graph convolutional network (GCN) which
aggregates features from similar samples across the domains. To prevent the
classifiers from over-fitting on its own noisy pseudo-labels we develop a
co-teaching strategy with the dual classifier head that is assisted by
curriculum learning to obtain more reliable pseudo-labels. Furthermore, when
the domain labels are available, we propose Domain-aware Curriculum Learning
(DCL), a sequential adaptation strategy that first adapts on the easier target
domains, followed by the harder ones. We experimentally demonstrate the
effectiveness of our proposed frameworks on several benchmarks and advance the
state-of-the-art in the MTDA by large margins (e.g. +5.6% on the DomainNet).
