Domain adversarial adaptation in a continual setting poses a significant challenge due to the limitations on accessing previous source domain data. Despite extensive research in continual learning, the task of adversarial adaptation cannot be effectively accomplished using only a small number of stored source domain data, which is a standard setting in memory replay approaches. This limitation arises from the erroneous empirical estimation of $\gH$-divergence with few source domain samples. To tackle this problem, we propose a double-head discriminator algorithm, by introducing an addition source-only domain discriminator that are trained solely on source learning phase. We prove that with the introduction of a pre-trained source-only domain discriminator, the empirical estimation error of $\gH$-divergence related adversarial loss is reduced from the source domain side. Further experiments on existing domain adaptation benchmark show that our proposed algorithm achieves more than 2$\%$ improvement on all categories of target domain adaptation task while significantly mitigating the forgetting on source domain.

在持续设置中的领域对抗适应存在一定挑战，由于无法访问以前的源域数据。与持续学习的广泛研究不同，仅仅使用少量存储的源域数据无法有效地完成对抗适应任务，这是内存回放方法中的常见设置。本文提出了一种双头鉴别器算法，通过引入一个仅在源学习阶段训练的仅源域鉴别器，证明了预训练的仅源域鉴别器的引入可以减少源域方面的实证估计误差。进一步的实验表明，在现有的领域适应基准上，我们提出的算法在所有目标领域适应任务的各个类别上实现了超过2%的改进，同时显着减轻了对源域的遗忘。

双头辨别器的持续领域对抗适应