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    科学领域:

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

    背景情况:

    • 无监督域调整 (UDA) 通常依赖于强大的源代码模型,这些模型是不切实际的.
    • 源数据可能是无法访问或低效的适应训练在现实世界的场景.
    • 现有的方法在UDA中与对抗性训练作斗争,导致模型退化.

    研究的目的:

    • 解决强大的无源域调整,只使用非强大的源模型和未标记的目标数据.
    • 开发一种方法,克服在UDA中对抗训练所造成的退化.
    • 在具有挑战性的域调整任务中提高模型稳定性和性能.

    主要方法:

    • 拟议的无源交替优化 (SFAO) 用于训练一个强大的目标模型,使用一个非强大的源模型.
    • 采用了交替训练策略,以尽量减少源域和对手目标域之间的差异.
    • 引入软约束对抗训练 (SCAT),以减轻对抗训练期间的伪标签错误.

    主要成果:

    • 在清洁和对抗数据上,SFAO显著提高了模型性能.
    • 提出的方法有效地解决了强大的无源代码域调整的挑战.
    • 经验发现表明,对抗性训练放大了UDA错误的缓解.

    结论:

    • 通过拟议的SFAO和SCAT方法,可以实现强大的无源域调整.
    • 该方法为缺乏可靠源模型或源数据的场景提供了实际解决方案.
    • 该研究表明,对于域调整的对抗性强度有显著的进步.