  Most domain adaptation methods focus on single-source-single-target
adaptation settings. 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 important to have: a feature extractor
that generalizes well across domains; and effective aggregation of features
from the labeled source and different unlabeled target domains. Towards the
first, we use the recently popular Transformer as a feature extraction
backbone. Towards the second, we use a co-teaching-based approach using a
dual-classifier head, one of which is based on the graph neural network. The
proposed approach 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.
After adapting on each target, samples with a softmax-based confidence score
greater than a threshold are added to the pseudo-source, thus aggregating
knowledge from different domains. However, softmax is not entirely trustworthy
as a confidence score and may generate a high score for unreliable samples if
trained for many iterations. To mitigate this effect, we adopt a reiterative
approach, where we reduce target adaptation iterations, however, reiterate
multiple times over the target domains. The experimental evaluation on the
Office-Home, Office-31 and DomainNet datasets shows significant improvement
over the existing methods. We have achieved 10.7$\%$ average improvement in
Office-Home dataset over the state-of-art methods.
