A Unified Framework for Unsupervised Domain Adaptation based on Instance
  Weighting
Jinjing Zhu Feiyang Ye Qiao Xiao Pengxin Guo Yu Zhang Qiang Yang
Despite the progress made in domain adaptation, solving Unsupervised Domain
Adaptation (UDA) problems with a general method under complex conditions caused
by label shifts between domains remains a formidable task. In this work, we
comprehensively investigate four distinct UDA settings including closed set
domain adaptation, partial domain adaptation, open set domain adaptation, and
universal domain adaptation, where shared common classes between source and
target domains coexist alongside domain-specific private classes. The prominent
challenges inherent in diverse UDA settings center around the discrimination of
common/private classes and the precise measurement of domain discrepancy. To
surmount these challenges effectively, we propose a novel yet effective method
called Learning Instance Weighting for Unsupervised Domain Adaptation (LIWUDA),
which caters to various UDA settings. Specifically, the proposed LIWUDA method
constructs a weight network to assign weights to each instance based on its
probability of belonging to common classes, and designs Weighted Optimal
Transport (WOT) for domain alignment by leveraging instance weights.
Additionally, the proposed LIWUDA method devises a Separate and Align (SA) loss
to separate instances with low similarities and align instances with high
similarities. To guide the learning of the weight network, Intra-domain Optimal
Transport (IOT) is proposed to enforce the weights of instances in common
classes to follow a uniform distribution. Through the integration of those
three components, the proposed LIWUDA method demonstrates its capability to
address all four UDA settings in a unified manner. Experimental evaluations
conducted on three benchmark datasets substantiate the effectiveness of the
proposed LIWUDA method.