In this paper, we introduce a realistic and challenging domain adaptation problem called Universal Semi-supervised Model Adaptation (USMA), which i) requires only a pre-trained source model, ii) allows the source and target domain to have different label sets, i.e., they share a common label set and hold their own private label set, and iii) requires only a few labeled samples in each class of the target domain. To address USMA, we propose a collaborative consistency training framework that regularizes the prediction consistency between two models, i.e., a pre-trained source model and its variant pre-trained with target data only, and combines their complementary strengths to learn a more powerful model. The rationale of our framework stems from the observation that the source model performs better on common categories than the target-only model, while on target-private categories, the target-only model performs better. We also propose a two-perspective, i.e., sample-wise and class-wise, consistency regularization to improve the training. Experimental results demonstrate the effectiveness of our method on several benchmark datasets.

翻译：本文提出了一种现实且具有挑战性的领域自适应问题——通用半监督模型自适应（USMA），其特点包括：i）仅需预训练的源模型；ii）允许源域与目标域具有不同的标签集合，即共享公共标签集并各自持有私有标签集；iii）目标域每个类别仅需少量标注样本。为解决USMA问题，我们提出一种协作一致性训练框架，该框架通过正则化两个模型的预测一致性（即预训练源模型及其仅基于目标数据训练的变体），并融合两者的互补优势以学习更强大的模型。该框架的理论基础源于以下观察：源模型在公共类别上的表现优于仅用目标数据训练的模型，而在目标私有类别上，仅用目标数据训练的模型表现更优。此外，我们提出从样本级与类别级两个视角进行一致性正则化以改进训练过程。实验结果表明，该方法在多个基准数据集上具有显著有效性。