Strong-Weak Integrated Semi-supervision for Unsupervised Single and
  Multi Target Domain Adaptation
Xiaohu Lu Hayder Radha
Unsupervised domain adaptation (UDA) focuses on transferring knowledge
learned in the labeled source domain to the unlabeled target domain. Despite
significant progress that has been achieved in single-target domain adaptation
for image classification in recent years, the extension from single-target to
multi-target domain adaptation is still a largely unexplored problem area. In
general, unsupervised domain adaptation faces a major challenge when attempting
to learn reliable information from a single unlabeled target domain. Increasing
the number of unlabeled target domains further exacerbate the problem rather
significantly. In this paper, we propose a novel strong-weak integrated
semi-supervision (SWISS) learning strategy for image classification using
unsupervised domain adaptation that works well for both single-target and
multi-target scenarios. Under the proposed SWISS-UDA framework, a strong
representative set with high confidence but low diversity target domain samples
and a weak representative set with low confidence but high diversity target
domain samples are updated constantly during the training process. Both sets
are fused to generate an augmented strong-weak training batch with
pseudo-labels to train the network during every iteration. The extension from
single-target to multi-target domain adaptation is accomplished by exploring
the class-wise distance relationship between domains and replacing the strong
representative set with much stronger samples from peer domains via peer
scaffolding. Moreover, a novel adversarial logit loss is proposed to reduce the
intra-class divergence between source and target domains, which is
back-propagated adversarially with a gradient reverse layer between the
classifier and the rest of the network. Experimental results based on three
benchmarks, Office-31, Office-Home, and DomainNet, show the effectiveness of
the proposed SWISS framework.