  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.
