Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class
  Bias
Wenyu Zhang Qingmu Liu Felix Ong Wei Cong Mohamed Ragab Chuan-Sheng Foo
Domain adaptation is a critical task in machine learning that aims to improve
model performance on a target domain by leveraging knowledge from a related
source domain. In this work, we introduce Universal Semi-Supervised Domain
Adaptation (UniSSDA), a practical yet challenging setting where the target
domain is partially labeled, and the source and target label space may not
strictly match. UniSSDA is at the intersection of Universal Domain Adaptation
(UniDA) and Semi-Supervised Domain Adaptation (SSDA): the UniDA setting does
not allow for fine-grained categorization of target private classes not
represented in the source domain, while SSDA focuses on the restricted
closed-set setting where source and target label spaces match exactly. Existing
UniDA and SSDA methods are susceptible to common-class bias in UniSSDA
settings, where models overfit to data distributions of classes common to both
domains at the expense of private classes. We propose a new prior-guided
pseudo-label refinement strategy to reduce the reinforcement of common-class
bias due to pseudo-labeling, a common label propagation strategy in domain
adaptation. We demonstrate the effectiveness of the proposed strategy on
benchmark datasets Office-Home, DomainNet, and VisDA. The proposed strategy
attains the best performance across UniSSDA adaptation settings and establishes
a new baseline for UniSSDA.