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

Protein Diffusion in the Membrane01:24

Protein Diffusion in the Membrane

4.3K
Proteins show rotational as well as lateral diffusion across the membrane. The lateral diffusion of proteins was confirmed through the cell fusion experiment where mouse and human cells were fused, resulting in hybrid cells. When the human and mouse cells fused, the specific membrane proteins on human and mouse cells were marked with the red and green-fluorescent markers, respectively. Initially, the red and green fluorescence was located on the respective hemisphere of the cell. As time...
4.3K

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

Repeatability of corneal diameter measured by Sirius versus IOLMaster 700 in myopic eyes: combined with consistency and relationship of sulcus-to-sulcus.

Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie·2026
Same author

Refining feature representation for accurate fundus lesion segmentation.

Medical physics·2026
Same author

A multifunctional organic additive with diversified elements (F, S, P, and N) for high-performance PEO/LiTFSI electrolytes.

Materials horizons·2026
Same author

Unleashing the Power of Text-to-Image Diffusion Models for Category-Agnostic Pose Estimation.

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

Replicon-based genome-wide CRISPR knockout screening for the identification of host factors involved in viral replication.

Nature communications·2025
Same author

Clinical efficacy of an improved orbicularis oculi muscle plication surgery for senile involutional lower eyelid entropion.

International ophthalmology·2025

相关实验视频

Updated: Jun 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

543

通过域自适应扩散进行无监督域调整.

Duo Peng, Qiuhong Ke, ArulMurugan Ambikapathi

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 15, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了域自适应扩散 (DAD) 和相互学习策略 (MLS),以应对无监督域自适应 (UDA) 的挑战. 这种新的方法有效地弥合了领域的差距,显著提高了模型在适应任务上的性能.

    更多相关视频

    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    8.9K
    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    00:10

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    8.2K

    相关实验视频

    Last Updated: Jun 21, 2025

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    543
    Visualizing Visual Adaptation
    04:43

    Visualizing Visual Adaptation

    Published on: April 24, 2017

    8.9K
    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules
    00:10

    Single-Molecule Tracking Microscopy - A Tool for Determining the Diffusive States of Cytosolic Molecules

    Published on: September 5, 2019

    8.2K

    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:

    • 无监督域名适应 (UDA) 面临重大挑战,原因是源域和目标域之间的分布差异很大.
    • 现有的扩散模型通常是为高斯分布转换为数据而设计的,而不是针对特定领域的分布转移.
    • 在分配转换期间保留源域数据语义对于准确的目标域分类至关重要.

    研究的目的:

    • 探索传播技术,以应对具有挑战性的UDA任务.
    • 开发一种能够在维护语义的同时逐步转换跨域数据分布的方法.
    • 在域过渡过程中增强分类模型的能力.

    主要方法:

    • 提出一个新的域自适应扩散 (DAD) 模块.
    • 纳入相互学习策略 (MLS) 以促进在域过渡期间的学习.
    • 将大型域间隙分解成更小,更容易管理的步骤.

    主要成果:

    • 拟议的DAD模块和MLS有效地将数据分布从源域转换为目标域.
    • 该方法使分类模型能够在域过渡过程中逐渐学习.
    • 在三个标准的UDA数据集上,在最先进的方法上表现出卓越的性能.

    结论:

    • DAD模块和MLS通过弥合域间的差距,成功地减轻了UDA的挑战.
    • 这种方法提高了分类模型适应目标领域的适应性.
    • 这种基于传播的战略为未来的UDA研究提供了有希望的方向.