  We propose a hypothesis disparity regularized mutual information
maximization~(HDMI) approach to tackle unsupervised hypothesis transfer -- as
an effort towards unifying hypothesis transfer learning (HTL) and unsupervised
domain adaptation (UDA) -- where the knowledge from a source domain is
transferred solely through hypotheses and adapted to the target domain in an
unsupervised manner. In contrast to the prevalent HTL and UDA approaches that
typically use a single hypothesis, HDMI employs multiple hypotheses to leverage
the underlying distributions of the source and target hypotheses. To better
utilize the crucial relationship among different hypotheses -- as opposed to
unconstrained optimization of each hypothesis independently -- while adapting
to the unlabeled target domain through mutual information maximization, HDMI
incorporates a hypothesis disparity regularization that coordinates the target
hypotheses jointly learn better target representations while preserving more
transferable source knowledge with better-calibrated prediction uncertainty.
HDMI achieves state-of-the-art adaptation performance on benchmark datasets for
UDA in the context of HTL, without the need to access the source data during
the adaptation.
