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相关概念视频

Self-Evaluation: Self-Enhancement and Self-Verification03:00

Self-Evaluation: Self-Enhancement and Self-Verification

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Social psychologists have documented that feeling good about ourselves and maintaining positive self-esteem is a powerful motivator of human behavior (Tavris & Aronson, 2008). In the United States, members of the predominant culture typically think very highly of themselves and view themselves as good people who are above average on many desirable traits (Ehrlinger, Gilovich, & Ross, 2005). Often, our behavior, attitudes, and beliefs are affected when we experience a threat to our...
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Purposive Learning01:22

Purposive Learning

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E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
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Understanding the Self01:28

Understanding the Self

473
The self is a central aspect of human identity, encompassing an individual’s beliefs, emotions, perceptions, and experiences. It is a cognitive and psychological construct that enables individuals to interpret their traits and behaviors, influencing how they perceive themselves and interact with the world. While personality consists of stable and enduring characteristics, the self is shaped by self-perception and social experiences. This distinction highlights the dynamic nature of the...
473
Role-Based Identity01:21

Role-Based Identity

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Role-based identities are central to understanding how individuals navigate social environments by adopting distinct self-conceptions aligned with various societal roles. These identities are not fixed traits but are constructed through personal actions and the social feedback individuals receive in context-specific interactions. Each social role, such as student, teacher, or friend, carries a set of expectations and norms that influence how people think, feel, and behave within that...
306
Personal Identity01:25

Personal Identity

672
Personal identity is the deeply felt sense of self that individuals cultivate over time, intricately woven from intrinsic qualities they consider essential to their existence—qualities such as morality, intelligence, and friendliness. These attributes serve as vital internal benchmarks, guiding individuals in evaluating whether their actions resonate with their true selves.When personal identity takes center stage in one's life, individuals often emphasize their distinctiveness,...
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Introspection01:29

Introspection

443
Introspection, long upheld as a reliable route to self-knowledge, involves examining one's thoughts, emotions, and mental processes. It underpins many psychological practices, from mindfulness meditation to psychotherapy and self-help strategies. However, empirical evidence challenges the accuracy of introspection as a means of understanding oneself.Limitations of Introspective InsightSeminal work by Nisbett and Wilson demonstrated that individuals are frequently unaware of the true causes...
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相关实验视频

Updated: May 2, 2026

From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data
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From Voxels to Knowledge: A Practical Guide to the Segmentation of Complex Electron Microscopy 3D-Data

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一个课程风格的自我训练方法,用于无源的语义细分.

Yuxi Wang, Jian Liang, Zhaoxiang Zhang

    IEEE transactions on pattern analysis and machine intelligence
    |July 24, 2024
    PubMed
    概括

    这项研究引入了一种新的课程式自训练方法,用于在语义细分中实现无源域调整. 该方法有效地将模型适应新的数据,而无需源数据,从而提高隐私和性能.

    科学领域:

    • 计算机科学 计算机科学
    • 人工智能的人工智能
    • 机器学习 机器学习

    背景情况:

    • 无源域调整 (SFDA) 对保护隐私的人工智能至关重要.
    • 传统的SFDA方法在特征对齐方面遇到了困难.
    • 在没有源数据的情况下调整模型带来了独特的挑战.

    研究的目的:

    • 开发一种用于无源域自适应语义细分的新方法.
    • 为了解决SFDA的特征对齐方面的局限性.
    • 增强模型适应性,以保护源数据的同时,目标域.

    主要方法:

    • 提出一个课程式的自我训练框架.
    • 使用课程风格的缩最小化知识提取.
    • 在课程学习中使用互补的伪标签 (积极和消极).
    • 实施信息传播计划,以减少域内差异.
    • 将方法扩展到一个黑子源模型场景.

    主要成果:

    • 在无源语义细分上实现了最先进的性能.
    • 在合成到真实和不利条件数据集上都表现出有效性.
    • 在具有挑战性的场景中验证了方法的稳定性,包括黑子设置.

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    结论:

    • 拟议的课程式自我训练对SFDA语义细分有效.
    • 该方法成功地在没有直接访问源数据的情况下适应模型.
    • 这项工作通过提供保护隐私和高性能解决方案来推动SFDA的发展.