Domain Prompt Tuning via Meta Relabeling for Unsupervised Adversarial Adaptation

Unsupervised adversarial domain adaptation (ADA) aims to learn domain-invariant features by confusing a domain discriminator. As training goes on, the feature distributions of source and target samples are increasingly aligned/indistinguishable. The discrimination capability of the domain discriminator w.r.t. those aligned samples deteriorates due to the domain label of each sample is still fixed all through the learning process, which thus cannot effectively further drive the feature learning. A recently proposed method named Re-enforceable Adversarial Domain Adaptation (RADA) [1] tend to re-energize the domain discriminator during the training by using dynamic domain labels. Specifically, RADA sets up a heuristic criterion and uses it to relabel the well aligned target domain samples as source domain samples on the fly. In our study, we identify a critical problem of RADA: it is a kind of heuristic domain data re-partition solution without explicitly serving the adaptation task itself, suggesting that the criteria of RADA on which sample should be relabeled is hard to decide. To address the problem, we revisit domain relabeling process from a perspective of prompt tuning, and introduce a meta-optimized learnable prompts into RADA to replace some hand-craft designs in dynamic relabeling process, which scheme is named as RADA-prompt. Particularly, we employ a module of meta-prompter, which learns to adaptively relabel the samples based on the objective of serving UDA task. To train the meta-prompter, we leverage a domain alignment measurement and a classification measurement as the meta optimization objective. Extensive experiments on multiple unsupervised domain adaptation benchmarks demonstrate the effectiveness and superiority of RADA-prompt, this scheme also achieves state-of-the-art performance.