  Domain shift is a fundamental problem in visual recognition which typically
arises when the source and target data follow different distributions. The
existing domain adaptation approaches which tackle this problem work in the
closed-set setting with the assumption that the source and the target data
share exactly the same classes of objects. In this paper, we tackle a more
realistic problem of open-set domain shift where the target data contains
additional classes that are not present in the source data. More specifically,
we introduce an end-to-end Progressive Graph Learning (PGL) framework where a
graph neural network with episodic training is integrated to suppress
underlying conditional shift and adversarial learning is adopted to close the
gap between the source and target distributions. Compared to the existing
open-set adaptation approaches, our approach guarantees to achieve a tighter
upper bound of the target error. Extensive experiments on three standard
open-set benchmarks evidence that our approach significantly outperforms the
state-of-the-arts in open-set domain adaptation.
