This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the performance boost may be limited when the discrepancy between the source and target domains is large or the target domain contains outliers. To explicitly address this issue, we propose the Adversarial self-superVised domain Adaptation network for the TARget domain (AVATAR) algorithm. It outperforms state-of-the-art UDA models by concurrently reducing domain discrepancy while enhancing discrimination through domain adversarial learning, self-supervised learning, and sample selection strategy for the target domain, all guided by deep clustering. Our proposed model significantly outperforms state-of-the-art methods on three UDA benchmarks, and extensive ablation studies and experiments demonstrate the effectiveness of our approach for addressing complex UDA tasks.

翻译：本文提出了一种针对未标记目标域数据进行预测的无监督域适应（UDA）方法，专门解决域差异显著时的复杂UDA任务。主流UDA模型旨在通过利用标记源域数据学习两个域并提升目标域判别能力。然而，当源域与目标域间差异较大或目标域包含异常值时，性能提升可能受限。为明确解决该问题，我们提出了面向目标域的对抗自监督域适应网络（AVATAR）算法。该算法通过深度聚类引导的域对抗学习、自监督学习及目标域样本选择策略，在减少域差异的同时增强判别能力，从而超越现有最优UDA模型。我们的模型在三个UDA基准测试中显著优于现有最优方法，大量消融研究与实验证明了该方法在处理复杂UDA任务中的有效性。