Universal domain adaptation (UniDA) has been proposed to transfer knowledge
learned from a label-rich source domain to a label-scarce target domain without
any constraints on the label sets. In practice, however, it is difficult to
obtain a large amount of perfectly clean labeled data in a source domain with
limited resources. Existing UniDA methods rely on source samples with correct
annotations, which greatly limits their application in the real world. Hence,
we consider a new realistic setting called Noisy UniDA, in which classifiers
are trained with noisy labeled data from the source domain and unlabeled data
with an unknown class distribution from the target domain. This paper
introduces a two-head convolutional neural network framework to solve all
problems simultaneously. Our network consists of one common feature generator
and two classifiers with different decision boundaries. By optimizing the
divergence between the two classifiers' outputs, we can detect noisy source
samples, find "unknown" classes in the target domain, and align the
distribution of the source and target domains. In an extensive evaluation of
different domain adaptation settings, the proposed method outperformed existing
methods by a large margin in most settings.

本文提出了一种名为 Noisy UniDA 的现实情况下的通用域适应解决方案，它使用卷积神经网络框架来检测源域中带有噪声的样本，查找目标域中的未知类别，并实现源域和目标域的分布对齐。在不同的领域适应设置的广泛评估中，所提出的方法在大多数设置中都比现有方法表现优异。