The performance of Multi-Source Unsupervised Domain Adaptation depends
significantly on the effectiveness of transfer from labeled source domain
samples. In this paper, we proposed an adversarial agent that learns a dynamic
curriculum for source samples, called Curriculum Manager for Source Selection
(CMSS). The Curriculum Manager, an independent network module, constantly
updates the curriculum during training, and iteratively learns which domains or
samples are best suited for aligning to the target. The intuition behind this
is to force the Curriculum Manager to constantly re-measure the transferability
of latent domains over time to adversarially raise the error rate of the domain
discriminator. CMSS does not require any knowledge of the domain labels, yet it
outperforms other methods on four well-known benchmarks by significant margins.
We also provide interpretable results that shed light on the proposed method.

本文提出了一种基于 Curriculum Manager 的适用于源域选择的对抗式学习方法，其通过动态调整与目标域最适配的样本来提高多源无监督域自适应性能。实验结果表明该方法在四个基准测试中均优于其他方法。

多源域自适应中的源选择课程管理器

Curriculum Manager for Source Selection in Multi-Source Domain  Adaptation

Domain adaptation considers the problem of generalising a model learnt using
data from a particular source domain to a different target domain. Often it is
difficult to find a suitable single source to adapt from, and one must consider
multiple sources. Using an unrelated source can result in sub-optimal
performance, known as the \emph{negative transfer}. However, it is challenging
to select the appropriate source(s) for classifying a given target instance in
multi-source unsupervised domain adaptation (UDA). We model source-selection as
an attention-learning problem, where we learn attention over sources for a
given target instance. For this purpose, we first independently learn
source-specific classification models, and a relatedness map between sources
and target domains using pseudo-labelled target domain instances. Next, we
learn attention-weights over the sources for aggregating the predictions of the
source-specific models. Experimental results on cross-domain sentiment
classification benchmarks show that the proposed method outperforms prior
proposals in multi-source UDA.

该论文探讨了如何选择多个源来进行领域适应，使用注意力学习算法解决了多源无监督领域适应中的源选择难题，并在情感分类基准测试中表现出更好的性能。

无监督领域自适应的多源注意力

Multi-source Attention for Unsupervised Domain Adaptation

We present SOSELETO (SOurce SELEction for Target Optimization), a new method
for exploiting a source dataset to solve a classification problem on a target
dataset. SOSELETO is based on the following simple intuition: some source
examples are more informative than others for the target problem. To capture
this intuition, source samples are each given weights; these weights are solved
for jointly with the source and target classification problems via a bilevel
optimization scheme. The target therefore gets to choose the source samples
which are most informative for its own classification task. Furthermore, the
bilevel nature of the optimization acts as a kind of regularization on the
target, mitigating overfitting. SOSELETO may be applied to both classic
transfer learning, as well as the problem of training on datasets with noisy
labels; we show state of the art results on both of these problems.

本文介绍了一个名为 SOSELETO 的新方法，该方法利用源数据集解决目标数据集上的分类问题。SOSELETO 是基于一种简单的直觉：针对目标问题，某些源示例比其他示例更具信息量。通过一个双层优化方案来解决这个问题，源示例的权重是共同解决的。因此，目标可以选择对其自己的分类任务最有信息的源示例。此外，优化的双层特性对目标具有一定的正则化作用，减轻了过度拟合问题。SOSELETO 可应用于传统的迁移学习，以及训练嘈杂标签数据集的问题；我们展示了这两个问题的最先进结果。