In this paper, we investigate a challenging unsupervised domain adaptation setting -- unsupervised model adaptation. We aim to explore how to rely only on unlabeled target data to improve performance of an existing source prediction model on the target domain, since labeled source data may not be available in some real-world scenarios due to data privacy issues. For this purpose, we propose a new framework, which is referred to as collaborative class conditional generative adversarial net to bypass the dependence on the source data. Specifically, the prediction model is to be improved through generated target-style data, which provides more accurate guidance for the generator. As a result, the generator and the prediction model can collaborate with each other without source data. Furthermore, due to the lack of supervision from source data, we propose a weight constraint that encourages similarity to the source model. A clustering-based regularization is also introduced to produce more discriminative features in the target domain. Compared to conventional domain adaptation methods, our model achieves superior performance on multiple adaptation tasks with only unlabeled target data, which verifies its effectiveness in this challenging setting.

翻译：本文研究了一种具有挑战性的无监督域自适应设定——无监督模型自适应。由于数据隐私问题，现实场景中标注的源数据可能无法获取，因此我们旨在探索如何仅依赖未标注的目标数据来提升现有源预测模型在目标域上的性能。为此，我们提出了一种新框架，称为协同类条件生成对抗网络，以绕过对源数据的依赖。具体而言，预测模型通过生成目标风格的数据进行改进，这为生成器提供了更精确的指导。因此，生成器与预测模型能够在没有源数据的情况下相互协作。此外，由于缺乏源数据的监督，我们提出了一种鼓励与源模型相似性的权重约束。同时引入了基于聚类的正则化方法，以在目标域中产生更具判别性的特征。与传统域自适应方法相比，我们的模型仅使用未标注的目标数据就在多个自适应任务上取得了更优的性能，验证了其在此挑战性设定下的有效性。