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相关概念视频

Stereotype Content Model02:16

Stereotype Content Model

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The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
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Labeling Emotion01:20

Labeling Emotion

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Emotional labeling is a cognitive process that involves identifying and naming one's emotions, such as anger, fear, happiness, or sadness. It allows individuals to recognize and express their internal emotional states, a critical aspect of emotional regulation and communication. Labeling emotions requires more than mere recognition; it also involves drawing upon memory and contextual cues to understand the current situation and apply a corresponding emotional label. For instance, feeling...
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Metacognition01:26

Metacognition

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Metacognition is a conscious process where individuals are aware of their cognitive and executive processes, such as planning before solving a problem or self-monitoring during reading. For instance, a writer may need help with composing a piece. The situation involves a writer who is working on a piece of writing, but while doing so, they realize that something is missing. They notice that their characters lack depth or details. This realization occurs because the writer is reflecting on their...
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Immunogold Electron Microscopy01:20

Immunogold Electron Microscopy

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Immunoelectron microscopy utilizes immunogold labeling of endogenous proteins with specific antibodies to detect and localize these proteins in cells and tissues. The procedure provides insights into the distribution and quantification of protein under different stimulation conditions offering clues about their functions. Conjugating highly electron-dense gold particles with primary or secondary antibodies allow antigen detection on and within cells, with high resolution and specificity.
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Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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Stereotype Threat and Self-fulfilling Prophecies02:09

Stereotype Threat and Self-fulfilling Prophecies

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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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相关实验视频

Updated: Jun 12, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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基于伪标签的实用半监督的超级培训,用于少量学习.

Xingping Dong, Tianran Ouyang, Shengcai Liao

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |September 20, 2024
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了基于伪标签的超级学习 (PLML) 对于少数射击学习 (FSL). 在半监督的元训练中,PLML有效地使用未标记的数据,以有限的标签提高FSL模型的性能.

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    相关实验视频

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    科学领域:

    • 机器学习 机器学习
    • 计算机科学 计算机科学

    背景情况:

    • 短暂学习 (FSL) 方法通常需要大量的标记数据来进行元训练,这限制了它们的实际应用.
    • 现有的FSL半监督超训练 (SSMT) 方法需要从未标记的数据中进行类意识的样本选择,这往往是不切实际的.

    研究的目的:

    • 为FSL提出一个实用的半监督的超级培训设置,使用真正没有标签的数据.
    • 引入一种新的超级培训框架,即基于伪标签的超级学习 (PLML),以有效地利用标签和未标签的数据.

    主要方法:

    • 一个分类器使用半监督学习 (SSL) 进行训练,用于为未标记的数据生成伪标签.
    • 短暂的任务是使用标记和伪标记数据构建的.
    • 采用一种新的微调方法,包括功能平滑和噪声抑制,用于训练FSL模型对潜在的噪音伪标签进行训练.

    主要成果:

    • 拟议的PLML框架有效地减轻了FSL模型中的性能下降,但标记数据有限.
    • 在FSL基准上,PLML显著优于现有的代表性SSMT模型.
    • 超训练方法也提高了几个SSL算法的性能.

    结论:

    • PLML提供了一种实用且有效的解决方案,用于半监督的超级培训,用于少量学习,特别是在具有有限标记数据的场景中.
    • 该框架能够利用真正没有标签的数据,使其适合于现实的应用.
    • 这种方法证明了伪标签在改进FSL和SSL方法方面的潜力.