  Domain adaptation aims to transfer knowledge from a domain with adequate
labeled samples to a domain with scarce labeled samples. Prior research has
introduced various open set domain adaptation settings in the literature to
extend the applications of domain adaptation methods in real-world scenarios.
This paper focuses on the type of open set domain adaptation setting where the
target domain has both private ('unknown classes') label space and the shared
('known classes') label space. However, the source domain only has the 'known
classes' label space. Prevalent distribution-matching domain adaptation methods
are inadequate in such a setting that demands adaptation from a smaller source
domain to a larger and diverse target domain with more classes. For addressing
this specific open set domain adaptation setting, prior research introduces a
domain adversarial model that uses a fixed threshold for distinguishing known
from unknown target samples and lacks at handling negative transfers. We extend
their adversarial model and propose a novel adversarial domain adaptation model
with multiple auxiliary classifiers. The proposed multi-classifier structure
introduces a weighting module that evaluates distinctive domain characteristics
for assigning the target samples with weights which are more representative to
whether they are likely to belong to the known and unknown classes to encourage
positive transfers during adversarial training and simultaneously reduces the
domain gap between the shared classes of the source and target domains. A
thorough experimental investigation shows that our proposed method outperforms
existing domain adaptation methods on a number of domain adaptation datasets.
