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.

翻译：本文概述了一种近期提出的动态域自适应方法——DIRA。该方法在采用名为弹性权重巩固的正则化策略的基础上，仅需极少样本即可取得最先进的域自适应结果。已有研究表明，DIRA在与无监督自适应技术的竞争中展现出同等竞争力。然而，该方法存在局限：自适应过程中所用的少量样本需依赖标签，这使得其本质上属于监督学习技术。本文提出对DIRA方法进行改进，使其实现自监督化，即消除对标签的需求。本研究所提方法将在后续工作中通过实验进行验证。