On Self-Supervised Dynamic Incremental Regularised Adaptation.

CoRR(2023)

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摘要
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.
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