  To transfer the knowledge learned from a labeled source domain to an
unlabeled target domain, many studies have worked on universal domain
adaptation (UniDA), where there is no constraint on the label sets of the
source domain and target domain. However, the existing UniDA methods rely on
source samples with correct annotations. Due to the limited resources in the
real world, it is difficult to obtain a large amount of perfectly clean labeled
data in a source domain in some applications. As a result, we propose a novel
realistic scenario named Noisy UniDA, in which classifiers are trained using
noisy labeled data from the source domain as well as unlabeled domain data from
the target domain that has an uncertain class distribution. A multi-head
convolutional neural network framework is proposed in this paper to address all
of the challenges faced in the Noisy UniDA at once. Our network comprises a
single common feature generator and multiple classifiers with various decision
bounds. We can detect noisy samples in the source domain, identify unknown
classes in the target domain, and align the distribution of the source and
target domains by optimizing the divergence between the outputs of the various
classifiers. The proposed method outperformed the existing methods in most of
the settings after a thorough analysis of the various domain adaption
scenarios. The source code is available at
\url{https://github.com/YU1ut/Divergence-Optimization}.
