Universal domain adaptation (UniDA) is a practical but challenging problem, in which information about the relation between the source and the target domains is not given for knowledge transfer. Existing UniDA methods may suffer from the problems of overlooking intra-domain variations in the target domain and difficulty in separating between the similar known and unknown class. To address these issues, we propose a novel Mutual Learning Network (MLNet) with neighborhood invariance for UniDA. In our method, confidence-guided invariant feature learning with self-adaptive neighbor selection is designed to reduce the intra-domain variations for more generalizable feature representation. By using the cross-domain mixup scheme for better unknown-class identification, the proposed method compensates for the misidentified known-class errors by mutual learning between the closed-set and open-set classifiers. Extensive experiments on three publicly available benchmarks demonstrate that our method achieves the best results compared to the state-of-the-arts in most cases and significantly outperforms the baseline across all the four settings in UniDA. Code is available at https://github.com/YanzuoLu/MLNet.

翻译：通用域自适应（UniDA）是一个实用但具有挑战性的问题，在该问题中，源域与目标域之间的关联信息未被提供以完成知识迁移。现有UniDA方法可能面临忽视目标域内域内变化以及难以区分相似已知类与未知类的问题。针对这些问题，我们提出了一种新颖的基于邻域不变性的互学习网络（MLNet）。该方法设计了基于置信度引导的自适应邻域选择不变特征学习，以减小域内变化，从而获得更泛化的特征表示。通过采用跨域混合方案以增强未知类识别能力，所提方法利用闭集分类器与开集分类器之间的互学习机制，对误识别的已知类错误进行补偿。在三个公开基准上的大量实验表明，我们的方法在大多数情况下取得了最优结果，并在UniDA的四种设定中均显著优于基线方法。代码发布于https://github.com/YanzuoLu/MLNet。