  Domain generalisation (DG) methods address the problem of domain shift, when
there is a mismatch between the distributions of training and target domains.
Data augmentation approaches have emerged as a promising alternative for DG.
However, data augmentation alone is not sufficient to achieve lower
generalisation errors. This project proposes a new method that combines data
augmentation and domain distance minimisation to address the problems
associated with data augmentation and provide a guarantee on the learning
performance, under an existing framework. Empirically, our method outperforms
baseline results on DG benchmarks.
