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.

翻译：无监督域适应（UDA）旨在无需目标域标签的情况下，通过两种主要技术弥合源域与目标域之间的差距：输入级对齐（如生成式建模和风格化）和特征级对齐（匹配特征图分布，例如梯度反转层）。受生成式建模在图像分类中取得成功的启发，风格化方法近年来被提出用于回归任务（如姿态估计）。然而，通过生成式建模和风格化实现的输入级对齐会引入额外开销和计算复杂度，限制其在真实世界域适应任务中的应用。为探究输入级对齐在域适应中的作用，我们提出以下问题：基于生成式建模的风格化对于视觉域适应中的回归任务是否必要？令人惊讶的是，我们发现与分类任务相比，输入对齐对回归任务的影响微乎其微。基于这一发现，我们开发了一种非参数化的特征级域对齐方法——隐式风格化（ImSty），无需计算密集的风格化和生成式建模即可在现有最佳回归任务上实现持续改进。我们的工作在生成式建模和风格化逐渐在域泛化领域流行的当下，对其作用进行了批判性评估。