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

Self-Schemas02:16

Self-Schemas

30.9K
In general, a schema is a mental construct consisting of a cluster or collection of related concepts (Bartlett, 1932). There are many different types of schemata, and they all have one thing in common: schemata are a method of organizing information that allows the brain to work more efficiently. When a schema is activated, the brain makes immediate assumptions about the person or object being observed.
30.9K
Stereotype Content Model02:16

Stereotype Content Model

13.9K
The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence...
13.9K

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

Neural stem cell‑derived exosomes transfer miR‑124‑3p into cells to inhibit glioma growth by targeting FLOT2.

International journal of oncology·2022
Same author

Guidewire simulation of endovascular intervention: A systematic review.

The international journal of medical robotics + computer assisted surgery : MRCAS·2022
Same author

Targeting attack hypergraph networks.

Chaos (Woodbury, N.Y.)·2022
Same author

Prophylactic and Therapeutic HPV Vaccines: Current Scenario and Perspectives.

Frontiers in cellular and infection microbiology·2022
Same author

Creating a Thermostable β-Glucuronidase Switch for Homogeneous Immunoassay by Disruption of Conserved Salt Bridges at Diagonal Interfaces.

Biochemistry·2022
Same author

Investigation of the Mechanism of hsa_circ_000 1429 Adsorbed miR-205 to Regulate KDM4A and Promote Breast Cancer Metastasis.

Contrast media & molecular imaging·2022
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
查看所有相关文章

相关实验视频

Updated: May 23, 2025

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

6.9K

基于重建的异常定位通过知识为基础的自我训练.

Cheng Qian, Xiaoxian Lao, Chunguang Li

    IEEE transactions on neural networks and learning systems
    |March 11, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究介绍了基于知识的自我训练 (KIST) 用于异常局部化. 通过使用专家知识,KIST提高了性能,以便在图像重建中更好地利用弱标记的异常样本.

    更多相关视频

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.3K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.6K

    相关实验视频

    Last Updated: May 23, 2025

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    6.9K
    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
    08:20

    Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

    Published on: October 27, 2023

    1.3K
    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
    07:15

    Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

    Published on: August 16, 2020

    6.6K

    科学领域:

    • 计算机视觉 计算机视觉
    • 机器学习 机器学习
    • 人工智能的人工智能

    背景情况:

    • 异常局部化在工业环境中至关重要.
    • 基于重建的方法是常见的,因为简单和可解释性.
    • 当前的方法往往没有充分利用现有的异常样本信息.

    研究的目的:

    • 通过有效使用弱标记的异常样本来提高异常局部化性能.
    • 将领域专家知识整合到基于重建的异常检测中.
    • 开发一种新的方法,利用专家洞察力利用正常和异常数据.

    主要方法:

    • 提出了一种基于重建的新方法:以知识为基础的自我训练 (KIST).
    • 将专家知识整合到一个自我培训框架中.
    • 利用弱标记的异常样本和专家知识来生成像素级别的伪标签.
    • 开发了一种新的损失函数,以指导基于伪标签的重建.

    主要成果:

    • 在各种数据集中展示了KIST的有效性.
    • 与现有方法相比,在异常局部化方面表现出显著的改进.
    • 验证了结合专家知识和弱标记异常数据的优势.

    结论:

    • 通过有效地整合专家知识,KIST为异常局部化提供了卓越的方法.
    • 该方法成功地利用弱标记的异常数据来提高性能.
    • 这项工作推进了基于重建的异常定位技术.