  Unsupervised domain adaptation (UDA) for image classification has made
remarkable progress in transferring classification knowledge from a labeled
source domain to an unlabeled target domain, thanks to effective domain
alignment techniques. Recently, in order to further improve performance on a
target domain, many Single-Target Active Domain Adaptation (ST-ADA) methods
have been proposed to identify and annotate the salient and exemplar target
samples. However, it requires one model to be trained and deployed for each
target domain and the domain label associated with each test sample. This
largely restricts its application in the ubiquitous scenarios with multiple
target domains. Therefore, we propose a Multi-Target Active Domain Adaptation
(MT-ADA) framework for image classification, named D3GU, to simultaneously
align different domains and actively select samples from them for annotation.
This is the first research effort in this field to our best knowledge. D3GU
applies Decomposed Domain Discrimination (D3) during training to achieve both
source-target and target-target domain alignments. Then during active sampling,
a Gradient Utility (GU) score is designed to weight every unlabeled target
image by its contribution towards classification and domain alignment tasks,
and is further combined with KMeans clustering to form GU-KMeans for diverse
image sampling. Extensive experiments on three benchmark datasets, Office31,
OfficeHome, and DomainNet, have been conducted to validate consistently
superior performance of D3GU for MT-ADA.
