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相关概念视频

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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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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Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

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Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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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...
378
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
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Classification of Systems-II01:31

Classification of Systems-II

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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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Aggregates Classification01:29

Aggregates Classification

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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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它需要两个:双分支增强模块用于域泛化.

Jingwei Li1, Yuan Li1, Jie Tan2

  • 1Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.

Neural networks : the official journal of the International Neural Network Society
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PubMed
概括

使用富里埃变换的域泛化 (DG) 方法与分布外数据进行斗争. 我们的双分支增强模块 (DBAM) 通过利用振幅和相位光谱来提高性能,从而提高了概括性.

关键词:
域名通用化 域名通用化富里叶变换是什么意思?测试时间适应.不确定性校准的不确定性

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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
  • 深度学习 (Deep Learning) 是一种深度学习.

背景情况:

  • 深度神经网络 (DNN) 在分布之外的数据上表现出性能退化.
  • 域泛化 (DG) 旨在通过从多个源域学习未见的目标域来提高模型的稳定性.
  • 现有的基于福里埃转换的GD方法不够解决域间隙,因为它们只专注于抑制源域特定信息.

研究的目的:

  • 提出一种新的双分支增强模块 (DBAM),有效地利用振幅和相位光谱来增强域泛化.
  • 通过建立源域和目标域之间的连接并增强域异性信息的利用来解决先前方法的局限性.

主要方法:

  • DBAM利用里叶变换,利用振幅和相位光谱.
  • 振幅分支结合了内部域振幅分布纠正 (IADR) 和跨域振幅迪里克莱混合 (CADM) 以培训稳定性和特征空间探索.
  • 阶段分支采用随机对称阶段扰动 (RSPP) 来提高域异性信息识别的稳定性.

主要成果:

  • 在域泛化任务中,DBAM显著优于最先进的 (SOTA) 方法.
  • 拟议的技术,如测试时间振幅原型校准 (TAPC),有效地减轻了评估期间的域差距.
  • 在四个基准上进行了广泛的实验,验证了DBAM方法的有效性和优越性.

结论:

  • 通过有效利用振幅和相谱,DBAM在域泛化方面取得了重大进展.
  • 拟议的模块增强了模型的稳定性和对未见数据的概括能力.
  • 在各种环境中,DBAM代表了提高深度神经网络可靠性的有希望的方向.