  For unsupervised domain adaptation (UDA), to alleviate the effect of domain
shift, many approaches align the source and target domains in the feature space
by adversarial learning or by explicitly aligning their statistics. However,
the optimization objective of such domain alignment is generally not
coordinated with that of the object classification task itself such that their
descent directions for optimization may be inconsistent. This will reduce the
effectiveness of domain alignment in improving the performance of UDA. In this
paper, we aim to study and alleviate the optimization inconsistency problem
between the domain alignment and classification tasks. We address this by
proposing an effective meta-optimization based strategy dubbed MetaAlign, where
we treat the domain alignment objective and the classification objective as the
meta-train and meta-test tasks in a meta-learning scheme. MetaAlign encourages
both tasks to be optimized in a coordinated way, which maximizes the inner
product of the gradients of the two tasks during training. Experimental results
demonstrate the effectiveness of our proposed method on top of various
alignment-based baseline approaches, for tasks of object classification and
object detection. MetaAlign helps achieve the state-of-the-art performance.
