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 \textbf{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
this https URL

我们提出了一个新颖的具有邻域不变性的相互学习网络方法（MLNet），通过自适应邻居选择和置信度引导的不变性特征学习来减少目标域内的内部变化，通过交叉领域混杂方案进行更好的未知类别识别，互学习闭合集和开放集分类器以弥补已知类别错误的识别。在三个公开基准测试中，广泛的实验证明我们的方法在大多数情况下均取得最佳结果，并且在 UniDA 的四个设置中显著优于基线。