  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.
