Abstract
By transferring knowledge from a source domain, the performance of deep
clustering on an unlabeled target domain can be improved. When achieving
this, traditional approaches make the assumption that adequate amount of
labeled data is available in a source domain. However, this assumption
is usually unrealistic in practice. The source domain should be
carefully selected to share some characteristics with the target domain,
and it can not be guaranteed that rich labeled samples are always
available in the selected source domain.
We propose a novel framework to improve deep clustering by transferring
knowledge from a source domain without any labeled data. To select
reliable instances in the source domain for transferring, we propose a
novel adaptive threshold algorithm to select low entropy instances. To
transfer important features of the selected instances, we propose a
feature-level domain adaptation network (FeatureDA) which cancels
unstable generation process. With extensive experiments, we validate
that our method effectively improves deep clustering, without using any
labeled data in the source domain. Besides, without using any labeled
data in the source domain, our method achieves competitive results,
compared to the state-of-the-art methods using labeled data in the
source domain.