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

Observational Learning01:12

Observational Learning

212
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
212
Reducing Line Loss01:18

Reducing Line Loss

173
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...
173
Associative Learning01:27

Associative Learning

447
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...
447
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.4K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.4K
Introduction to Learning01:18

Introduction to Learning

474
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
474
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.8K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
1.8K

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

Rethinking the detail-preserved completion of complex tubular structures based on point cloud: A dataset and a benchmark.

Medical image analysis·2026
Same author

Global burden of bipolar disorder in 204 countries and territories, 1990-2021: a systematic analysis of temporal trends, demographic disparities, and SDI associations for the Global Burden of Disease Study 2021.

Psychiatry research·2026
Same author

Advancement of deep learning models with whole slide image in diagnosis, subtyping and prognosis for glioma.

Progress in biomedical engineering (Bristol, England)·2026
Same author

Deep Learning Assisted Motion Behavior Analysis of Catalytic Micromotors Based on Trajectory and Optical Flow.

Nano letters·2026
Same author

Genetic Diversity and Evolutionary Dynamics of Feline Panleukopenia Virus in China: Phylogenetic Analysis and Substitution Patterns in NS1 and VP2 Proteins.

Viruses·2026
Same author

Re-innervation of neuromuscular junctions by a conductive polypyrrole/silk fibroin/GelMA hydrogel facilitated functional skeletal muscle regeneration following volumetric muscle loss.

Journal of orthopaedic translation·2026
Same journal

Change-Prior-Guided Unsupervised Change Detection of Heterogeneous Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

AgonicDreamer: Enhancing Multi-View Consistency in Text-to-3D Generation via Rectified Score Distillation.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

BiCM-Prompt: Bidirectional Cross-Modal Prompt Tuning for Class-Incremental Learning on Multisource Remote Sensing Images.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

GoP-based Quality Enhancement on Video Compression.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Align then Tensorize: Multi-Level Consistent Anchor Graph Learning for Scalable Multi-View Clustering.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same journal

Beyond Fidelity: Diverse Image Synthesis via Retrieval-Augmented Diffusion.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
查看所有相关文章

相关实验视频

Updated: Jul 23, 2025

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

574

重新平衡的零射击学习

Zihan Ye, Guanyu Yang, Xiaobo Jin

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |July 19, 2023
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了一个新的框架,以解决零射击学习 (ZSL) 中不平衡的语义预测. 拟议的再平衡平均平方误差 (ReMSE) 损失有效地减轻了预测偏差,改善了ZSL的性能.

    更多相关视频

    Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice
    06:04

    Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

    Published on: March 4, 2014

    21.1K
    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
    08:05

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

    Published on: January 5, 2018

    9.8K

    相关实验视频

    Last Updated: Jul 23, 2025

    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

    574
    Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice
    06:04

    Study Motor Skill Learning by Single-pellet Reaching Tasks in Mice

    Published on: March 4, 2014

    21.1K
    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers
    08:05

    A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

    Published on: January 5, 2018

    9.8K

    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:

    • 零射击学习 (ZSL) 识别了没有培训样本的未见类.
    • 当前的ZSL方法经常遭受不平衡的语义预测,对一些语义表现良好,但对其他语义表现不佳.
    • 传统的不平衡学习方法不足以应对ZSL的独特挑战.

    研究的目的:

    • 引入适合ZSL的不平衡学习框架.
    • 解决不平衡的ZSL的独特挑战,包括标签价值相关性和多样化的错误分布.
    • 为了提高ZSL模型的准确性和稳定性.

    主要方法:

    • 正式化ZSL作为一个不平衡的回归问题来理解语义标签的影响.
    • 提出了一个新的重权损失函数:再平衡平均平方误差 (ReMSE).
    • ReMSE追踪错误分布的平均值和差异,用于均衡的课堂学习.

    主要成果:

    • 经验证据表明,语义标签如何导致ZSL中的不平衡预测.
    • ReMSE有效地缓解了各类语义预测不平衡.
    • 广泛的实验表明,拟议的方法优于最先进的ZSL技术.

    结论:

    • 提出的不平衡的学习框架和ReMSE损失为ZSL提供了一个理论上合理的解决方案.
    • 通过解决固有的预测失衡,ReMSE显著提高了ZSL的性能.
    • 这项工作通过提供更强大,更准确的方法来推进ZSL领域.