  Towards better unsupervised domain adaptation (UDA). Recently, researchers
propose various domain-conditioned attention modules and make promising
progresses. However, considering that the configuration of attention, i.e., the
type and the position of attention module, affects the performance
significantly, it is more generalized to optimize the attention configuration
automatically to be specialized for arbitrary UDA scenario. For the first time,
this paper proposes EvoADA: a novel framework to evolve the attention
configuration for a given UDA task without human intervention. In particular,
we propose a novel search space containing diverse attention configurations.
Then, to evaluate the attention configurations and make search procedure
UDA-oriented (transferability + discrimination), we apply a simple and
effective evaluation strategy: 1) training the network weights on two domains
with off-the-shelf domain adaptation methods; 2) evolving the attention
configurations under the guide of the discriminative ability on the target
domain. Experiments on various kinds of cross-domain benchmarks, i.e.,
Office-31, Office-Home, CUB-Paintings, and Duke-Market-1510, reveal that the
proposed EvoADA consistently boosts multiple state-of-the-art domain adaptation
approaches, and the optimal attention configurations help them achieve better
performance.
