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English(EN) Efficient unsupervised domain adaptation via self-supervised vision transformer and synergistic cross-domain alignment

新的EUDA框架为AI模型提供参数高效的域自适应

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一个名为EUDA的参数高效框架，用于无监督域自适应，旨在解决源域和目标域之间数据分布差异的挑战。这种新方法利用冻结的DINOv2骨干网络作为特征提取器，仅更新轻量级的瓶颈层和分类头。EUDA还包含一个协同域对齐损失（SDAL），它结合了交叉熵和最大均值差异，以改进判别式学习和跨域对齐，同时显著减少可训练参数，并展现出具有竞争力的性能。 AI

影响 通过降低计算成本，这种方法可以实现AI模型在资源受限环境中的更高效部署。

排序理由 关于无监督域自适应新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.AI 阅读 →

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新的EUDA框架为AI模型提供参数高效的域自适应

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关于无监督域自适应新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.AI TIER_1 English(EN) · Ali Abedi, Q. M. Jonathan Wu, Ning Zhang, Farhad Pourpanah ·

    通过自监督视觉变换器和协同跨域对齐实现高效无监督域自适应

    arXiv:2407.21311v2 Announce Type: replace-cross Abstract: Unsupervised domain adaptation (UDA) aims to mitigate domain shift, where the distribution of labeled source data differs from that of unlabeled target data. Despite recent advances, existing methods often rely on fine-tun…

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