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SETA:用于域名通用化的语义意识边缘引导令牌增强.

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    本研究介绍了语义意识的边缘引导的令牌增强 (SETA),以改善视觉转换器和MLP的域概括. 通过保留全球特征,同时扰乱本地边缘线索,实现最先进的结果,SETA增强了形状学习.

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    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 域泛化 (DG) 旨在提高对未见数据域的模型稳定性.
    • 目前用于DG的数据增强方法对于基于令牌的架构,如视觉变压器 (ViT) 和多层感知器 (MLP),往往是不理想的.
    • 现有的增强技术主要集中在卷积神经网络 (CNN) 上,忽视了基于令牌的模型的独特特征.

    研究的目的:

    • 为解决基于令牌模型的域泛化中现有增强方法的局限性.
    • 提出一种新的增强技术,增强整体形状信息的学习.
    • 在领域转移的背景下,提高ViT和MLP模型的稳定性和概括能力.

    主要方法:

    • 提出语义意识的边缘引导令牌增强 (SETA),这是一个新的域泛化方法.
    • 在符号表示中,SETA会扰乱本地边缘线索,同时保留全球形状特征.
    • 开发SETA的风格化变体,并与最先进的风格增强技术相结合,以提高概括性.

    主要成果:

    • 与基于代币架构的现有方法相比,SETA表现出优越的性能.
    • 提出的方法有效地提高了模型学习整体形状信息的能力.
    • 五个基准的实验表明,SETA在各种ViT和MLP模型上取得了最先进的结果.
    • 理论分析证实了SETA在降低概括风险的有效性.

    结论:

    • 通过专注于形状信息,SETA显著改善了基于令牌的模型的域概括.
    • 该方法提供了一个强大的解决方案,用于在域移动下增强模型性能.
    • 对于现代深度学习架构的数据增强策略来说,SETA是一个有前途的进步.