  In Domain Adaptation (DA), the category-relevant losses usually occupy a
dominant position, while they are usually built with hard or soft labels in
existing models. We observed that hard labels are overconfident due to hard
samples existed, and soft labels are ambiguous as too many small noisy
probabilities involved, and both of them are easily to cause negative transfer.
Besides, the category-irrelevant losses in Closed-Set DA (CSDA) paradigm fail
to work in Open-Set DA (OSDA), and they also have to be in a category-relevant
form, since target data samples are split into shared and private classes. To
this end, we propose a newly-unified DA framework (i.e., Importance Filtered
Cross-Domain Adaptation, IFCDA). Firstly, an importance filtered mechanism is
devised to generate filtered soft labels to mitigate negative transfer
desirably. Specifically, the soft labels are divided into confident and
ambiguous ones. Then, only the maximum probability in each confident label is
retained, and a threshold value is set to truncate each ambiguous label so that
only prominent probabilities are reserved. Moreover, a general graph-based
label propagation is contrived to attain soft labels in both CSDA and OSDA,
where an extra component is embedded into label vector, so that it could detect
target novel classes. Finally, the category-relevant losses in both scenarios
are reformulated using filtered soft labels, while the category-irrelevant MMD
loss in CSDA is reformulated as a form like class-wise MMD using newly-designed
importance filtered soft labels. Notably, CSDA paradigm is a special case when
all extra components are set to 0, thus the proposed approach is geared to both
CSDA and OSDA. Comprehensive experiments on benchmark cross-domain object
recognition datasets verify that the proposed approach outperforms several
state-of-the-art methods in both scenarios.
