  In theory, the success of unsupervised domain adaptation (UDA) largely relies
on domain gap estimation. However, for source free UDA, the source domain data
can not be accessed during adaptation, which poses great challenge of measuring
the domain gap. In this paper, we propose to use many classifiers to learn the
source domain decision boundaries, which provides a tighter upper bound of the
domain gap, even if both of the domain data can not be simultaneously accessed.
The source model is trained to push away each pair of classifiers whilst
ensuring the correctness of the decision boundaries. In this sense, our many
classifiers model separates the source different categories as far as possible
which induces the maximum disagreement of many classifiers in the target
domain, thus the transferable source domain knowledge is maximized. For
adaptation, the source model is adapted to maximize the agreement among pairs
of the classifiers. Thus the target features are pushed away from the decision
boundaries. Experiments on several datasets of UDA show that our approach
achieves state of the art performance among source free UDA approaches and can
even compete to source available UDA methods.
