We introduce a new problem in unsupervised domain adaptation, termed as Generalized Universal Domain Adaptation (GUDA), which aims to achieve precise prediction of all target labels including unknown categories. GUDA bridges the gap between label distribution shift-based and label space mismatch-based variants, essentially categorizing them as a unified problem, guiding to a comprehensive framework for thoroughly solving all the variants. The key challenge of GUDA is developing and identifying novel target categories while estimating the target label distribution. To address this problem, we take advantage of the powerful exploration capability of generative flow networks and propose an active domain adaptation algorithm named GFlowDA, which selects diverse samples with probabilities proportional to a reward function. To enhance the exploration capability and effectively perceive the target label distribution, we tailor the states and rewards, and introduce an efficient solution for parent exploration and state transition. We also propose a training paradigm for GUDA called Generalized Universal Adversarial Network (GUAN), which involves collaborative optimization between GUAN and GFlowNet. Theoretical analysis highlights the importance of exploration, and extensive experiments on benchmark datasets demonstrate the superiority of GFlowDA.

翻译：我们提出了一种无监督域自适应中的新问题，称为广义通用域自适应（GUDA），旨在实现对包括未知类别在内的所有目标标签的精确预测。GUDA 弥合了基于标签分布偏移和标签空间不匹配的变体之间的差距，将它们本质地归类为一个统一的问题，从而引导出一个能够全面解决所有变体的综合性框架。GUDA 的主要挑战在于开发并识别新的目标类别，同时估计目标标签分布。为了解决这个问题，我们利用生成流网络强大的探索能力，提出了一种名为 GFlowDA 的主动域自适应算法，该算法根据与奖励函数成比例的概率选择多样化的样本。为了增强探索能力并有效感知目标标签分布，我们定制了状态和奖励，并引入了一种高效的父节点探索和状态转换解决方案。我们还为 GUDA 提出了一种名为广义通用对抗网络（GUAN）的训练范式，该范式涉及 GUAN 与 GFlowNet 之间的协同优化。理论分析强调了探索的重要性，而在基准数据集上的大量实验则展示了 GFlowDA 的优越性。