@article{Le_Akmeliawati_Carneiro_2021, title={Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)}, volume={35}, url={https://ojs.aaai.org/index.php/AAAI/article/view/17907}, DOI={10.1609/aaai.v35i18.17907}, abstractNote={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.}, number={18}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Le, Hoang Son and Akmeliawati, Rini and Carneiro, Gustavo}, year={2021}, month={May}, pages={15821-15822} }