  In conventional domain adaptation, a critical assumption is that there exists
a fully labeled domain (source) that contains the same label space as another
unlabeled or scarcely labeled domain (target). However, in the real world,
there often exist application scenarios in which both domains are partially
labeled and not all classes are shared between these two domains. Thus, it is
meaningful to let partially labeled domains learn from each other to classify
all the unlabeled samples in each domain under an open-set setting. We consider
this problem as weakly supervised open-set domain adaptation. To address this
practical setting, we propose the Collaborative Distribution Alignment (CDA)
method, which performs knowledge transfer bilaterally and works collaboratively
to classify unlabeled data and identify outlier samples. Extensive experiments
on the Office benchmark and an application on person reidentification show that
our method achieves state-of-the-art performance.
