Prior Unsupervised Domain Adaptation (UDA) methods often aim to train a
domain-invariant feature extractor, which may hinder the model from learning
sufficiently discriminative features. To tackle this, a line of works based on
prompt learning leverages the power of large-scale pre-trained vision-language
models to learn both domain-invariant and specific features through a set of
domain-agnostic and domain-specific learnable prompts. Those studies typically
enforce invariant constraints on representation, output, or prompt space to
learn such prompts. Differently, we cast UDA as a multiple-objective
optimization problem in which each objective is represented by a domain loss.
Under this new framework, we propose aligning per-objective gradients to foster
consensus between them. Additionally, to prevent potential overfitting when
fine-tuning this deep learning architecture, we penalize the norm of these
gradients. To achieve these goals, we devise a practical gradient update
procedure that can work under both single-source and multi-source UDA.
Empirically, our method consistently surpasses other prompt-based baselines by
a large margin on different UDA benchmarks

在无监督领域自适应中，通过基于提示学习的方法，使用大规模预训练的视觉 - 语言模型来学习领域不变和领域特定的特征，并将领域无关的约束转化为优化问题，通过梯度对齐和梯度范数惩罚来实现不同领域的一致性，从而在单源和多源无监督领域自适应任务中取得了优异的实验结果。