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相关概念视频

Associative Learning01:27

Associative Learning

345
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...
345
The Ideal Transformer01:26

The Ideal Transformer

380
In single-phase two-winding transformers, two windings are coiled around a magnetic core characterized by cross-sectional area A and magnetic permeability μ. A phasor current i1 enters the left winding while i2 exits the right winding, establishing the fundamental working of the transformer through electromagnetic principles.
Ampere's Law forms the basis of understanding the magnetic field within the transformer. It states that the integral of the magnetic field intensity's...
380
Types Of Transformers01:16

Types Of Transformers

971
Transformers can provide desired voltages to a circuit by modifying the number of turns in the secondary windings.
If the ratio of the number of turns in the secondary winding to that of the primary winding is greater than one, then the transformer is said to be a step-up transformer. In a step-up transformer, the voltage at the secondary winding is greater than the voltage applied at the primary winding.
However, if this ratio is less than one, the transformer is said to be a step-down...
971
Transformers01:26

Transformers

1.1K
A device that transforms voltages from one value to another using induction is called a transformer. A transformer consists of two separate coils, or windings, wrapped around the same soft iron core. However, they are electrically insulated from each other.
The iron core has a substantial relative permeability. Therefore, the magnetic field lines generated due to the current in one winding are almost entirely confined within the core, such that the same magnetic flux permeates each turn of both...
1.1K
Observational Learning01:12

Observational Learning

168
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...
168
Transformers with Off-Nominal Turns Ratios01:25

Transformers with Off-Nominal Turns Ratios

151
In scenarios involving parallel transformers with disparate ratings, developing per-unit models requires accommodating off-nominal turns ratios. This situation arises when the selected base voltages are not proportional to the transformer’s voltage ratings. Consider a transformer where the rated voltages are related by the term a. If the chosen voltage bases satisfy a relationship involving term b, term c is defined as the ratio of these bases. This ratio is then substituted into the...
151

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ZS-VAT:学习无偏向的属性知识,通过视觉属性转换器实现零射击识别.

Zongyan Han, Zhenyong Fu, Shuo Chen

    IEEE transactions on neural networks and learning systems
    |April 23, 2024
    PubMed
    概括

    本研究引入了一种新的视觉属性转换器,用于零射击学习 (ZSL),以解决有偏见的属性知识. ZS-VAT模型有效地学习了无偏的属性知识,提高了零射击识别性能.

    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:

    • 零射击学习 (ZSL) 依赖于属性知识来进行知识传递.
    • 现有的ZSL方法通常学习有偏见的属性知识,阻碍识别性能.

    研究的目的:

    • 提出一种新的视觉属性变压器,用于零射击识别 (ZS-VAT).
    • 学习无偏见的属性知识,提高ZSL的性能.

    主要方法:

    • 开发了一种属性头自我注意力 (AHSA) 机制,以学习无偏的属性知识.
    • 引入了属性融合模型 (AFM) 来从属性知识中恢复类别知识.
    • 结合属性嵌入预测 (AEP) 和全球嵌入预测 (GEP) 进行最终的语义预测.

    主要成果:

    • ZS-VAT有效地学习了无偏的属性知识,减少了属性之间的相互影响.
    • AHSA和AFM展示了协同增强知识的方法.
    • 拟议的方案在两种通用ZSL (GZSL) 的基准数据集上取得了最先进的结果.

    结论:

    • 在ZSL中,ZS-VAT提供了一种有效和可解释的解决方案,用于学习ZSL中不偏见的属性知识.

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  • 与现有的GZSL方法相比,拟议的方法显著提高了零射击识别性能.