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

On Self-Supervised Dynamic Incremental Regularised Adaptation

Domain adaptation (DA) enables knowledge transfer from a labeled source
domain to an unlabeled target domain by reducing the cross-domain distribution
discrepancy. Most prior DA approaches leverage complicated and powerful deep
neural networks to improve the adaptation capacity and have shown remarkable
success. However, they may have a lack of applicability to real-world
situations such as real-time interaction, where low target inference latency is
an essential requirement under limited computational budget. In this paper, we
tackle the problem by proposing a dynamic domain adaptation (DDA) framework,
which can simultaneously achieve efficient target inference in low-resource
scenarios and inherit the favorable cross-domain generalization brought by DA.
In contrast to static models, as a simple yet generic method, DDA can integrate
various domain confusion constraints into any typical adaptive network, where
multiple intermediate classifiers can be equipped to infer "easier" and
"harder" target data dynamically. Moreover, we present two novel strategies to
further boost the adaptation performance of multiple prediction exits: 1) a
confidence score learning strategy to derive accurate target pseudo labels by
fully exploring the prediction consistency of different classifiers; 2) a
class-balanced self-training strategy to explicitly adapt multi-stage
classifiers from source to target without losing prediction diversity.
Extensive experiments on multiple benchmarks are conducted to verify that DDA
can consistently improve the adaptation performance and accelerate target
inference under domain shift and limited resources scenarios

本文提出了一种动态域自适应框架（DDA），该框架可以在低资源情况下实现高效的目标推理，并继承域适应所带来的有利泛化能力，同时应用领域混淆约束来提高适应性能。