Deep Domain-Adversarial Image Generation for Domain Generalisation

Understand

Machine learning models typically suffer from the domain shift problem when trained on a source dataset and evaluated on a target dataset of different distribution.

• To overcome this problem, domain generalisation (DG) methods aim to leverage data from multiple source domains so that a trained model can generalise to unseen domains.
• In this paper, we propose a novel DG approach based on \emph{Deep Domain-Adversarial Image Generation} (DDAIG).
• Specifically, DDAIG consists of three components, namely a label classifier, a domain classifier and a domain transformation network (DoTNet).