用切片水氏距离提升图像风格解耦，改善无监督域适应性能
Sliced Wasserstein Discrepancy in Disentangling Representation and Adaptation Networks for Unsupervised Domain Adaptation
- 用切片水氏距离替代传统格拉姆矩阵，更精准捕捉风格差异
- 在数字分类与驾驶场景分割任务上，准确率显著提升
- 适合关注特征对齐与风格迁移的视觉领域研究者
本文提出DRANet-SWD，一种用于无监督域适应的完整图像内容与风格解耦框架。该方法在DRANet基础上引入切片水氏差异（SWD）作为风格损失，替代传统的格拉姆矩阵损失。通过实验验证，SWD能更稳健地比较特征分布，在数字分类和驾驶场景分割数据集上均表现更优。结果表明，相较于格拉姆矩阵，SWD在建模风格变化方面具有更强的统计鲁棒性，有助于实现更优的风格自适应。这些发现证明了SWD在优化特征对齐与提升域适应性能方面的有效性。
原文摘要 · Abstract (English)
This paper introduces DRANet-SWD as a novel complete pipeline for disentangling content and style representations of images for unsupervised domain adaptation (UDA). The approach builds upon DRANet by incorporating the sliced Wasserstein discrepancy (SWD) as a style loss instead of the traditional Gram matrix loss. The potential advantages of SWD over the Gram matrix loss for capturing style variations in domain adaptation are investigated. Experiments using digit classification datasets and driving scenario segmentation validate the method, demonstrating that DRANet-SWD enhances performance. Results indicate that SWD provides a more robust statistical comparison of feature distributions, leading to better style adaptation. These findings highlight the effectiveness of SWD in refining feature alignment and improving domain adaptation tasks across these benchmarks. Our code can be found here.
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