  Convolutional Neural Networks (CNNs) have brought revolutionary advances to
many research areas due to their capacity of learning from raw data. However,
when those methods are applied to non-controllable environments, many different
factors can degrade the model's expected performance, such as unlabeled
datasets with different levels of domain shift and category shift.
Particularly, when both issues occur at the same time, we tackle this
challenging setup as Open Set Domain Adaptation (OSDA) problem. In general,
existing OSDA approaches focus their efforts only on aligning known classes or,
if they already extract possible negative instances, use them as a new category
learned with supervision during the course of training. We propose a novel way
to improve OSDA approaches by extracting a high-confidence set of unknown
instances and using it as a hard constraint to tighten the classification
boundaries of OSDA methods. Especially, we adopt a new loss constraint
evaluated in three different means, (1) directly with the pristine negative
instances; (2) with randomly transformed negatives using data augmentation
techniques; and (3) with synthetically generated negatives containing
adversarial features. We assessed all approaches in an extensive set of
experiments based on OVANet, where we could observe consistent improvements for
two public benchmarks, the Office-31 and Office-Home datasets, yielding
absolute gains of up to 1.3% for both Accuracy and H-Score on Office-31 and
5.8% for Accuracy and 4.7% for H-Score on Office-Home.
