In this paper, we overview a recent 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 discuss a proposed alteration to the DIRA method to make it self-supervised i.e. remove the need for providing labels. Experiments on our proposed alteration will be provided in future work.

本文概述了最近一种名为DIRA的动态领域自适应方法，该方法依靠一些样本和弹性权重整合（elastic weight consolidation）这种正则化方法来实现最先进的（SOTA）领域自适应结果。DIRA已经被先前研究证明能够与SOTA的无监督适应技术竞争。然而，DIRA的一个局限性是它依赖于为适应中使用的少量样本提供标签，这使其成为一种有监督技术。本文讨论了对DIRA方法的改进提议，以使其成为自监督的方法，即不需要提供标签。我们将在未来的工作中提供对我们提议的改进的实验结果。

自监督动态增量正则化适应