Reducing Distributional Uncertainty by Mutual Information Maximisation and Transferable Feature Learning
Distributional uncertainty exists broadly in many real-world applications, one of which in the form of domain discrepancy. Yet in the existing literature, the mathematical definition of it is missing. In this paper, we propose to formulate the distributional uncertainty both between the source(s) and target domain(s) and within each domain using mutual information. Further, to reduce distributional uncertainty (e.g. domain discrepancy), we (1) maximise the mutual information between source and target domains and (2) propose a transferable feature learning scheme, balancing two complementary and discriminative feature learning processes (general texture learning and self-supervised transferable shape learning) according to the uncertainty. We conduct extensive experiments on both domain adaption and domain generalisation using challenging common benchmarks: Office-Home and DomainNet. Results show the great effectiveness of the proposed method and its superiority over the state-of-the-art methods. "