Abstract
Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. The most recent UDA methods always resort to adversarial training to yield state-of-the-art results and a dominant number of existing UDA methods employ convolutional neural networks (CNNs) as feature extractors to learn domain invariant features. Vision transformer (ViT) has attracted tremendous attention since its emergence and has been widely used in various computer vision tasks, such as image classification, object detection, and semantic segmentation, yet its potential in adversarial domain adaptation has never been investigated. In this paper, we fill this gap by employing the ViT as the feature extractor in adversarial domain adaptation. Moreover, we empirically demonstrate that ViT can be a plug-and-play component in adversarial domain adaptation, which means directly replacing the CNN-based feature extractor in existing UDA methods with the ViT-based feature extractor can easily obtain performance improvement. The code is available at this https URL.
Abstract (translated)
无监督领域适应（UDA）的目的是将来自标注源域的知识转移到未标注的目标域。最最新的UDA方法总是依赖于对抗性训练以获得最先进的成果和主导数量现有的UDA方法采用卷积神经网络（CNN）作为特征提取器来学习域不变的特征。自Vision Transformer（ViT） emergence以来，已经引起了巨大的关注，并在各种计算机视觉任务中得到了广泛应用，然而其在对抗领域适应性的潜在能量从未被研究。在本文中，我们通过将ViT作为对抗领域适应的特征提取器来填补这一空白。此外，我们通过实验实证证明，ViT可以成为对抗领域适应的一个插件，这意味着在现有UDA方法中，将基于CNN的特征提取器直接替换为ViT的特征提取器可以轻松获得性能提升。代码可在此处下载：https://url.com/
URL
https://arxiv.org/abs/2404.15817
