Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)

• URL: http://arxiv.org/abs/2012.13973v1
• Date: Sun, 27 Dec 2020 16:50:40 GMT
• Title: Domain Generalisation with Domain Augmented Supervised Contrastive Learning (Student Abstract)
• Authors: Hoang Son Le, Rini Akmeliawati, Gustavo Carneiro
• Abstract summary: 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. Empirically, our method outperforms baseline results on DG benchmarks.
• Score: 17.865068872754293
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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