A3：面向无源域适应的主动对抗对齐方法
A3: Active Adversarial Alignment for Source-Free Domain Adaptation
- Oklahoma State University（俄克拉荷马州立大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对无源无监督域适应中伪标签噪声大、分布偏移难处理的问题，提出结合自监督、对抗训练与主动学习的A3框架，通过主动采样、对抗损失与一致性正则化实现无源数据的分布对齐，提升域适应效果。
AI中文摘要：
无监督域适应（UDA）旨在将知识从有标注的源域迁移到无标注的目标域。近期研究聚焦于无源UDA场景，即仅能获取目标域数据，该场景颇具挑战性，因为模型依赖存在噪声的伪标签，且难以应对分布偏移问题。我们提出主动对抗对齐（A3）框架，这是一种结合自监督学习、对抗训练与主动学习的新型框架，用于实现鲁棒的无源UDA。A3通过采集函数主动筛选信息量大、多样性高的样本用于训练，借助对抗损失与一致性正则化完成模型适配，在无需访问源数据的情况下实现分布对齐。A3通过协同整合主动学习与对抗学习，实现了有效的域对齐与噪声抑制，推动了无源UDA领域的发展。
英文摘要：
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. Recent works have focused on source-free UDA, where only target data is available. This is challenging as models rely on noisy pseudo-labels and struggle with distribution shifts. We propose Active Adversarial Alignment (A3), a novel framework combining self-supervised learning, adversarial training, and active learning for robust source-free UDA. A3 actively samples informative and diverse data using an acquisition function for training. It adapts models via adversarial losses and consistency regularization, aligning distributions without source data access. A3 advances source-free UDA through its synergistic integration of active and adversarial learning for effective domain alignment and noise reduction.
