In the context of single domain generalisation, the objective is for models
that have been exclusively trained on data from a single domain to demonstrate
strong performance when confronted with various unfamiliar domains. In this
paper, we introduce a novel model referred to as Contrastive Uncertainty Domain
Generalisation Network (CUDGNet). The key idea is to augment the source
capacity in both input and label spaces through the fictitious domain generator
and jointly learn the domain invariant representation of each class through
contrastive learning. Extensive experiments on two Single Source Domain
Generalisation (SSDG) datasets demonstrate the effectiveness of our approach,
which surpasses the state-of-the-art single-DG methods by up to $7.08\%$. Our
method also provides efficient uncertainty estimation at inference time from a
single forward pass through the generator subnetwork.

通过引入一种新型模型 Contrastive Uncertainty Domain Generalisation Network (CUDGNet) ，本文提出了在单一域上训练的模型能够在面对多个陌生领域时展现出强大性能的方法。该方法通过虚拟域生成器增强输入和标签空间的源容量，并通过对比学习来联合学习每个类别的域不变表示。在两个 Single Source Domain Generalisation (SSDG) 数据集上的大量实验证明了我们方法的有效性，超过了最先进的单 - DG 方法高达 7.08%。我们的方法还能够通过生成子网络的单次前向传递有效地进行不确定性估计。