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相关概念视频

Associative Learning01:27

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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.
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Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
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What do you think is the single most influential factor in determining with whom you become friends and whom you form romantic relationships? You might be surprised to learn that the answer is simple: the people with whom you have the most contact. This most important factor is proximity. You are more likely to be friends with people you have regular contact with. For example, there are decades of research that shows that you are more likely to become friends with people who live in your dorm,...
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
Two variables, for example, a and b, are said to be positively correlated if both variables move in the same direction. In other words, a positive correlation exists between two variables, a and b, if:
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在CNN模型中通过关系嵌入卷积层学习特征关系.

Shengzhou Xiong1, Yihua Tan1, Guoyou Wang1

  • 1School of Artificial Intelligence and Automation, Huazhong University of Science and Technology, Wuhan, 430074, China; National Key Laboratory of Multispectral Information Intelligent Processing Technology, Wuhan, 430074, China.

Neural networks : the official journal of the International Neural Network Society
|July 18, 2024
PubMed
概括
此摘要是机器生成的。

本研究介绍了一种用于卷积神经网络 (CNN) 的新方法,可以在没有额外数据或模块的情况下学习视觉特征之间的关系. 该方法提高了可解释性,域泛化和效率,优于现有方法.

关键词:
卷积神经网络是一种卷积神经网络.域名通用化 域名通用化特性关系学习的特征关系学习.推理加速的推理加速.可以解释性 解释性噪声强度 噪声强度 噪声强度

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科学领域:

  • 计算机视觉 计算机视觉
  • 人工智能的人工智能
  • 认知科学 认知科学

背景情况:

  • 卷积神经网络 (CNN) 在层次特征提取方面表现出色,但忽视特征关系,限制可解释性和域泛化.
  • 现有的方法来结合特征关系往往需要外部先验知识或辅助模块,增加计算和存储成本.
  • 人类认知有效地利用层次的视觉属性关系,这是标准CNN缺少的能力.

研究的目的:

  • 为了使CNN能够学习层次深度特征之间的关系,而不需要先前的知识或增加计算/存储资源.
  • 在可解释性,域泛化和稳定性等领域提高CNN的基本性能.
  • 解决特征关系学习的关键挑战:量化连接强度,识别无用的连接,并更新关系图.

主要方法:

  • 定义了CNN中等级特征之间的学习关系的任务.
  • 提出了关系嵌入卷积 (RE-Conv) 层,用于在卷积层中表示特征关系.
  • 引入了"使用和不使用"策略来管理连接强度,无用的连接和关系图的更新.

主要成果:

  • 在解释性,域泛化,噪声稳定性和推断效率方面取得了显著的改进.
  • 在域泛化任务中超越了最先进的方法.
  • 实现了与标准CNN相提并论的精度,同时减少了约50%的浮点运算 (FLOP).

结论:

  • 拟议的特征关系学习方案有效地提高了CNN的性能,而不需要额外的资源消耗.
  • 该方法为改善CNN提供了实用和灵活的解决方案,特别是在域泛化方面.
  • 与现有方法的无集成允许进一步提高性能.