  In this paper, we give an overview of a recently developed method for dynamic
domain adaptation, named DIRA, which relies on a few samples in addition to a
regularisation approach, named elastic weight consolidation, to achieve
state-of-the-art (SOTA) domain adaptation results. DIRA has been previously
shown to perform competitively with SOTA unsupervised adaption techniques.
However, a limitation of DIRA is that it relies on labels to be provided for
the few samples used in adaption. This makes it a supervised technique. In this
paper, we propose a modification to the DIRA method to make it self-supervised
i.e. remove the need for providing labels. Our proposed approach will be
evaluated experimentally in future work.
