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

Difference from Background: Limit of Detection01:05

Difference from Background: Limit of Detection

The limit of detection (LOD) is the smallest amount of analyte that can be distinguished from the background noise. The LOD value corresponds to the concentration at which the analyte signal is three times larger than the standard deviation of the blank signal. Below this value, the analyte signal cannot be differentiated from the background noise. It is calculated by dividing the calibration slope by 3 times the standard deviation of the blank signals.
The LOD indicates the presence or absence...

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

Increased copy number of mitochondrial DNA predicts poor prognosis of esophageal squamous cell carcinoma.

Oncology letters·2018
Same author

Del17p does not always significantly influence the survival of B-cell chronic lymphoproliferative disorders.

Oncotarget·2018
Same author

Processing bulk natural wood into a high-performance structural material.

Nature·2018
Same author

Retrospective study of paraneoplastic neurological syndromes in a Chinese Han population from Shandong, East China.

The International journal of neuroscience·2018
Same author

Genetics of Magnesium Disorders.

Kidney diseases (Basel, Switzerland)·2018
Same author

Efficacy of Rituximab for Patients with Chronic Lymphocytic Leukemia.

Zhongguo yi xue ke xue yuan xue bao. Acta Academiae Medicinae Sinicae·2018

相关实验视频

Updated: May 28, 2026

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
08:25

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

8.9K

通过有约束的选择性对抗性学习进行准确和强大的对象检测.

Jianpin Chen, Heng Li, Qi Gao

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |October 4, 2024
    PubMed
    概括

    有限制的选择性对抗性学习 (SALC) 增强了对清洁和损坏图像的对象检测网络. 这种方法可以提高准确性和稳定性,而无需额外的数据或成本.

    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:

    • 基于卷积神经网络 (ConvNet) 的物体探测器在清洁图像方面表现出色,但在损坏的数据 (噪音,模糊,恶劣天气) 上却失败.
    • 这种性能差距限制了安全敏感应用程序的可行性.
    • 当前的稳定性方法通常需要额外的标记数据,图像恢复或降低清洁图像的性能.

    研究的目的:

    • 开发一种通用培训方法,即有约束的选择性对抗性学习 (SALC),同时提高物体检测器的精度和稳定性.
    • 为了解决经常与对抗训练相关的清洁图像的性能下降.

    主要方法:

    • 提出了多任务对抗学习中对抗样本的统一配方,以使训练数据多样化.
    • 引入了一个批量本地比较策略,其中有两个批量规范化 (BN) 分支,通过分析模型偏差和BN统计数据来平衡准确性和稳定性.
    • 实施任务意识比率值,以管理子任务损失和防止性能恶化.

    主要成果:

    • 在清洁基准 (Pascal VOC,MS-COCO) 和腐败基准 (Pascal VOC-C,MS-COCO-C) 上,SALC取得了最先进的结果.
    • 在不影响清洁图像性能的情况下,表现出对各种图像损坏的强度提高.

    更多相关视频

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    483
    A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
    06:25

    A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

    Published on: May 16, 2025

    110

    相关实验视频

    Last Updated: May 28, 2026

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment
    08:25

    Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

    Published on: May 7, 2019

    8.9K
    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
    03:31

    Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

    Published on: December 15, 2023

    483
    A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
    06:25

    A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

    Published on: May 16, 2025

    110
  • 在没有额外的标记数据,推断成本或模型参数的情况下,验证了该方法对各种探测器的适用性.

    • 结论:

    • 萨尔克为提高物体检测网络的稳定性和精度提供了一种新且有效的解决方案.
    • 该方法提供了一种实用且高效的方法,可以在现实世界,具有挑战性的条件下提高探测器性能.
    • 萨尔克在开发可靠的计算机视觉系统方面取得了重大进展.