  Universal domain adaptation (UniDA) has been proposed to transfer knowledge
learned from a label-rich source domain to a label-scarce target domain without
any constraints on the label sets. In practice, however, it is difficult to
obtain a large amount of perfectly clean labeled data in a source domain with
limited resources. Existing UniDA methods rely on source samples with correct
annotations, which greatly limits their application in the real world. Hence,
we consider a new realistic setting called Noisy UniDA, in which classifiers
are trained with noisy labeled data from the source domain and unlabeled data
with an unknown class distribution from the target domain. This paper
introduces a two-head convolutional neural network framework to solve all
problems simultaneously. Our network consists of one common feature generator
and two classifiers with different decision boundaries. By optimizing the
divergence between the two classifiers' outputs, we can detect noisy source
samples, find "unknown" classes in the target domain, and align the
distribution of the source and target domains. In an extensive evaluation of
different domain adaptation settings, the proposed method outperformed existing
methods by a large margin in most settings.
