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。

MLNet: 具有邻域不变性的互学习网络用于通用域适应

MLNet: Mutual Learning Network with Neighborhood Invariance for  Universal Domain Adaptation

This paper considers the domain adaptive person re-identification (re-ID)
problem: learning a re-ID model from a labeled source domain and an unlabeled
target domain. Conventional methods are mainly to reduce feature distribution
gap between the source and target domains. However, these studies largely
neglect the intra-domain variations in the target domain, which contain
critical factors influencing the testing performance on the target domain. In
this work, we comprehensively investigate into the intra-domain variations of
the target domain and propose to generalize the re-ID model w.r.t three types
of the underlying invariance, i.e., exemplar-invariance, camera-invariance and
neighborhood-invariance. To achieve this goal, an exemplar memory is introduced
to store features of the target domain and accommodate the three invariance
properties. The memory allows us to enforce the invariance constraints over
global training batch without significantly increasing computation cost.
Experiment demonstrates that the three invariance properties and the proposed
memory are indispensable towards an effective domain adaptation system. Results
on three re-ID domains show that our domain adaptation accuracy outperforms the
state of the art by a large margin. Code is available at:
this https URL

本文探讨了领域自适应人员再识别问题，并提出了相应的解决方案，通过引入范例内、摄像头内和邻域内不变量，结合样本存储器实现全局训练批次的不变量约束，得以有效应用于实际应用中。