Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Reducing Line Loss01:18

Reducing Line Loss

351
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss in...
351
Improving Translational Accuracy02:07

Improving Translational Accuracy

14.0K
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...
14.0K
Improving Translational Accuracy02:07

Improving Translational Accuracy

3.5K
3.5K
Mean Absolute Deviation01:13

Mean Absolute Deviation

3.3K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
3.3K
Line Loss01:10

Line Loss

491
The different configurations of source-load connections include wye (star) and delta connections. The relationship between line and phase voltages and currents varies depending on the configuration. When the source is supplying power, it is transmitted through the wires to the load, and during this transmission, some power is absorbed by the wires, leading to line loss.
Line loss impacts power delivery efficiency in a balanced three-phase circuit. The symmetry in such a circuit simplifies the...
491
Weighted Mean00:57

Weighted Mean

6.2K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
6.2K

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Agentic Designer: Progressive Multi-Agent Collaboration for Structure-Aware Interior Layout Generation.

IEEE transactions on pattern analysis and machine intelligence·2026
Same author

Ellagic Acid-Based MOFs with Synergistic Adsorption and ROS-Driven Photocatalysis for Water Purification.

Inorganic chemistry·2026
Same author

Association of Triglyceride-Glucose-Frailty Index with Cardiovascular Disease and All-Cause Mortality Incidence in Individuals with Cardiovascular-Kidney-Metabolic Syndrome Stages 0-3: A Nationwide Prospective Cohort Study.

Journal of clinical medicine·2026
Same author

Formal synthesis of protosappanin A.

Natural product research·2026
Same author

Hard-Soft Gradient-Engineered Oxychloride Coating on Ni-Rich Cathodes for All-Solid-State Lithium Batteries.

ACS nano·2026
Same author

Mitigating Internal Gliding of a High-Voltage O3-Type Cathode via Na-Site Doping with High Ionic Potential Cations.

Nano letters·2026

相关实验视频

Updated: Jan 11, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K

对于深度度度度学习的代理AN损失.

Wenjie Peng1, Quhui Ke1, Jinglin Liang1

  • 1School of Electronic and Information Engineering, South China University of Technology, Guangzhou, 510641, China.

Neural networks : the official journal of the International Neural Network Society
|November 9, 2025
PubMed
概括

一个新的Proxy-Anchor-Negative (Proxy-AN) 损失通过平衡样本和代理表示来改善深度度度度学习. 这种方法提高了对优质嵌入空间的类内紧性和样本可区分性.

关键词:
深度度指标学习 (deep metric learning) 是一种深度度指标学习.图像检索 图像检索 图像检索基于代理的损失基于代理的损失代表性的学习学习.

更多相关视频

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

相关实验视频

Last Updated: Jan 11, 2026

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

1.7K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.9K

科学领域:

  • 计算机科学 计算机科学
  • 机器学习 机器学习
  • 人工智能的人工智能

背景情况:

  • 深度度度学习使用代理表示来近似类分布,旨在简化培训和加快融合.
  • 现有的基于代理的损失被分为以样本为中心或以代理为中心的,每个都有在平衡样本和代理表示质量的限制.
  • 低于最佳的嵌入空间是由于忽视了以样本为中心的损失中的代理忠实性,以及缺乏以样本为中心的损失中的样本可辨别性.

研究的目的:

  • 介绍一种新的损失函数,即Proxy-Anchor-Negative (Proxy-AN),它调和了样本中心和代理中心损失的分离焦点.
  • 结合两种方法的优势,实现代理和样本的表示质量的整体提升.
  • 在深度度度度学习中,促进学习更具歧视性的指标.

主要方法:

  • 代理AN损失采用了一个以代理为中心的方法,用于正对,通过将代理与正样本对齐来增强类内紧性.
  • 对于负对,采用以样本为中心的方法,通过将样本与负代理保持距离来提高样本的可区分性.
  • 这种协同策略确保了代理和样本表示的平衡改进.

主要成果:

  • 对主流图像检索基准数据集的广泛实验表明,与领先的度量学习算法相比,这些算法得到了实质性的改进.
  • 该方法在各种场景中显示出卓越的性能,包括部分训练数据和类不平衡设置.
  • 代理AN损失有效地提高了代理忠诚度和样本可区分性,从而改善了嵌入空间.

结论:

  • 拟议的Proxy-AN损失有效地平衡了深度度度度学习中的以样本为中心和代理为中心的策略.
  • 这种新的方法在图像检索任务中带来了显著的性能提升,超过了现有的最先进的方法.
  • 代理AN损失在各种数据条件中表现出稳定性和卓越性能,包括类不平衡和部分数据设置.