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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
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装甲:保护无法学习的例子免受数据增强的影响.

Xueluan Gong, Yuji Wang, Yanjiao Chen

    IEEE transactions on pattern analysis and machine intelligence
    |January 12, 2026
    PubMed
    概括

    数据增强可以破坏由无法学习的例子保护的私人数据,使深度神经网络 (DNN) 能够学习敏感信息. ARMOR框架有效地防范这些隐私侵犯,确保数据即使在增强后仍然无法学习.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 数据 隐私 数据 隐私 数据

    背景情况:

    • 在线私人数据容易受到未经授权的收集,用于训练深度神经网络 (DNN).
    • 无法学习的例子旨在通过尽量减少DNN训练损失来保护数据,使数据难以学习.
    • 数据增强是一种常见的预处理步骤,可以无意中恢复受保护数据中的隐私.

    研究的目的:

    • 调查和揭示数据增强所带来的隐私侵犯风险,使用无法学习的例子.
    • 提出一种新的防御框架,ARMOR,以防止数据增强引起的隐私侵犯.
    • 在没有直接访问模型培训过程的情况下,开发保护数据隐私的方法.

    主要方法:

    • 一个非本地模块辅助的替代模型被设计用于模拟数据增强效应.
    • 开发了一个代孕增强选择策略,以优化每个类别的增强.
    • 动态步骤大小调整算法用于产生防御噪声.
    • 在4个数据集和5种增强方法上进行了广泛的实验.

    主要成果:

    • 数据增强显著提高了模型准确度,无法学习的例子从21.3%增加到66.1%.
    • 武器成功地保留了受保护的私人数据对数据增强的不可学习性.

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  • 与基线方法相比,ARMOR可将测试准确度降低高达60%.
  • 拟议的防御框架证明了对抗对手训练的稳定性.
  • 结论:

    • 当应用于无法学习的数据时,数据增强对隐私构成重大威胁.
    • 亚马尔框架为这些隐私漏洞提供了有效的防御机制.
    • 在机器学习管道中,ARMOR提供了一个强大的解决方案来保护私人数据,即使使用复杂的预处理技术.