Is Generative Modeling-based Stylization Necessary for Domain Adaptation
  in Regression Tasks?
Jinman Park Francois Barnard Saad Hossain Sirisha Rambhatla Paul Fieguth
Unsupervised domain adaptation (UDA) aims to bridge the gap between source
and target domains in the absence of target domain labels using two main
techniques: input-level alignment (such as generative modeling and stylization)
and feature-level alignment (which matches the distribution of the feature
maps, e.g. gradient reversal layers). Motivated from the success of generative
modeling for image classification, stylization-based methods were recently
proposed for regression tasks, such as pose estimation. However, use of
input-level alignment via generative modeling and stylization incur additional
overhead and computational complexity which limit their use in real-world DA
tasks. To investigate the role of input-level alignment for DA, we ask the
following question: Is generative modeling-based stylization necessary for
visual domain adaptation in regression? Surprisingly, we find that
input-alignment has little effect on regression tasks as compared to
classification. Based on these insights, we develop a non-parametric
feature-level domain alignment method -- Implicit Stylization (ImSty) -- which
results in consistent improvements over SOTA regression task, without the need
for computationally intensive stylization and generative modeling. Our work
conducts a critical evaluation of the role of generative modeling and
stylization, at a time when these are also gaining popularity for domain
generalization.