  Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging
domain adaptation setting where the feature spaces of source and target domains
are heterogeneous, and the target domain has only unlabeled data. Existing HUDA
methods assume that both positive and negative examples are available in the
source domain, which may not be satisfied in some real applications. This paper
addresses a new challenging setting called positive and unlabeled heterogeneous
unsupervised domain adaptation (PU-HUDA), a HUDA setting where the source
domain only has positives. PU-HUDA can also be viewed as an extension of PU
learning where the positive and unlabeled examples are sampled from different
domains. A naive combination of existing HUDA and PU learning methods is
ineffective in PU-HUDA due to the gap in label distribution between the source
and target domains. To overcome this issue, we propose a novel method,
predictive adversarial domain adaptation (PADA), which can predict likely
positive examples from the unlabeled target data and simultaneously align the
feature spaces to reduce the distribution divergence between the whole source
data and the likely positive target data. PADA achieves this by a unified
adversarial training framework for learning a classifier to predict positive
examples and a feature transformer to transform the target feature space to
that of the source. Specifically, they are both trained to fool a common
discriminator that determines whether the likely positive examples are from the
target or source domain. We experimentally show that PADA outperforms several
baseline methods, such as the naive combination of HUDA and PU learning.
