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通过基于增强的样本查询和渐进式模型适应的无源主动域调整.

Shuang Li, Rui Zhang, Kaixiong Gong

    IEEE transactions on neural networks and learning systems
    |December 21, 2023
    PubMed
    概括

    无源主动域调整 (SFADA) 通过在没有源数据的情况下查询目标标签来解决数据隐私问题. SQAdapt有效地选择信息化目标样本并调整模型,优于现有方法.

    科学领域:

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

    背景情况:

    • 主动域调整 (ADA) 通过使用有限的标记目标数据来增强无监督域调整 (UDA).
    • 由于隐私和安全问题,现实世界的应用程序面临源数据可访问性的挑战.
    • 无源源主动域调整 (SFADA) 是作为一个实用的设置,在源数据是不可用的介绍.

    研究的目的:

    • 提出一种新的方法,SQAdapt,用于具有挑战性的SFADA环境.
    • 解决信息化目标样本查询和在没有源数据的情况下减轻域差距的关键挑战.
    • 开发一种统一的方法,以便在无源场景中有效地适应模型.

    主要方法:

    • SQAdapt使用具有数据增强的主动选择模块 (ASM) 来查询基于灵敏度和不确定性的信息目标样本.
    • 一个分类器适应模块 (CAM) 使用标记和未标记的目标数据逐步校准分类器重量.
    • 分布对齐模块 (DAM) 使用类似源的目标样本作为替代品,在缺少源数据的情况下对分布对齐.

    主要成果:

    • 在SFADA任务中,SQAdapt实现了卓越的性能.
    • 拟议的方法在没有源数据的情况下有效地减轻域间隙.

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  • SQAdapt的性能超过了大多数传统的主动域调整方法的性能.

    • 结论:

    • 对于实际的SFADA问题,SQAdapt提供了一个统一而有效的解决方案.
    • 该方法的主动标签查询策略是通用的,可以与其他SFUDA方法集成.
    • SQAdapt显著提升了无源代码域调整的最新技术.