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相关概念视频

Associative Learning01:27

Associative Learning

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Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
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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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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
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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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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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弱增强指导关系自主监督学习

Mingkai Zheng, Shan You, Fei Wang

    IEEE transactions on pattern analysis and machine intelligence
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    概括
    此摘要是机器生成的。

    关系式自主监督学习 (ReSSL) 模拟不同实例之间的关系,优于现有的方法. 这种新的方法增强了视觉表示学习,没有手动数据注释.

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    科学领域:

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

    背景情况:

    • 自主监督学习 (SSL) 擅长从未标记的数据中学习视觉表示.
    • 目前的SSL方法主要关注实例级特征,忽视实例间关系.

    研究的目的:

    • 引入一种新的自我监督学习范式,即关系式自我监督学习 (ReSSL),该范式模拟不同实例之间的关系.
    • 通过捕捉实例间动态来增强视觉表示学习.

    主要方法:

    • 开发了一个ReSSL框架,使用对对相似性的敏分布作为关系度量.
    • 为了可靠的关系表示,采用弱增强和效率的势头策略.
    • 整合了一个不对称的预测器头和InfoNCE热身策略,以提高稳健性和性能.

    主要成果:

    • 拟议的ReSSL框架在与最先进的方法相比显示出更高的性能.
    • 在各种网络架构中观察到显著的改进,包括像EfficientNet和MobileNet这样的轻量级模型.

    结论:

    • ReSSL为自我监督的视觉表示学习提供了一个强大的新范式.
    • 模拟实例间关系对于推进SSL技术至关重要.