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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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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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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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跨类和跨域语义增强用于域泛化.

Mengzhu Wang, Yuehua Liu, Jianlong Yuan

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 23, 2024
    PubMed
    概括

    本研究引入了类间和域间语义增强 (CDSA),以改善域泛化. 通过新的语义增强技术,CDSA通过增加源域多样性来提高未见数据上的模型性能.

    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:

    • 域泛化旨在创建适用于新,未见的数据域的模型.
    • 数据增强,特别是语义层次的方法,提高了域泛化,但往往缺乏源域多样性.
    • 现有的语义增强技术在类内和域内采样方向,限制了它们的有效性.

    研究的目的:

    • 提出一种新的方法,即跨类和跨域语义增强 (CDSA),以增强域名泛化.
    • 解决当前语义级数据增强方法中源域多样性不足的局限性.

    主要方法:

    • 介绍了CrossSmooth,这是一种基于采样的方法,可以从类间变化中生成语义方向.
    • 开发了CrossVariance以通过采样这些类间语义方向来捕捉各种域风格.
    • 实现了CDSA用于域泛化任务.

    主要成果:

    • 在四个基准数据集上证明了CDSA的显著有效性:数字-DG,PACS,Office-Home和DomainNet.
    • 在对语义细分数据集 (GTAV,SYNTHIA,Cityscapes,Mapillary和BDDS) 进行CDSA验证时取得了实质性的改进.

    结论:

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    • 通过增加源域多样性,CDSA有效地提高了域泛化.
    • 拟议的方法在各种计算机视觉任务中显示了广泛的适用性和显著的性能增长.