  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.
