Continual Domain Adversarial Adaptation via Double-Head Discriminators
<|reference_start|>Continual Domain Adversarial Adaptation via Double-Head Discriminators: 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.<|reference_end|>
@article{shen2024continual,
    title={Continual Domain Adversarial Adaptation via Double-Head Discriminators},
    author={Yan Shen and Zhanghexuan Ji and Chunwei Ma and Mingchen Gao},
    journal={arXiv preprint arXiv:2402.03588},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.03588},
    primaryClass={cs.LG cs.AI}
}