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.

翻译：本文概述了一种名为DIRA的最新动态域自适应方法，该方法通过依赖少量样本并结合弹性权重巩固的正则化策略，实现了最先进的域自适应结果。已有研究表明，DIRA的性能可与最先进的无监督域自适应技术相媲美。然而，DIRA的一个局限性在于其自适应过程所需的少量样本必须提供标签，这使其成为一种监督学习方法。本文讨论了对DIRA方法的一项改进方案，旨在实现自监督特性——即消除对标签的需求。关于该改进方案的实验验证将在后续工作中呈现。