  Most existing methods for unsupervised domain adaptation (UDA) rely on a
shared network to extract domain-invariant features. However, when facing
multiple source domains, optimizing such a network involves updating the
parameters of the entire network, making it both computationally expensive and
challenging, particularly when coupled with min-max objectives. Inspired by
recent advances in prompt learning that adapts high-capacity models for
downstream tasks in a computationally economic way, we introduce Multi-Prompt
Alignment (MPA), a simple yet efficient framework for multi-source UDA. Given a
source and target domain pair, MPA first trains an individual prompt to
minimize the domain gap through a contrastive loss. Then, MPA denoises the
learned prompts through an auto-encoding process and aligns them by maximizing
the agreement of all the reconstructed prompts. Moreover, we show that the
resulting subspace acquired from the auto-encoding process can easily
generalize to a streamlined set of target domains, making our method more
efficient for practical usage. Extensive experiments show that MPA achieves
state-of-the-art results on three popular datasets with an impressive average
accuracy of 54.1% on DomainNet.
