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

Associative Learning01:27

Associative Learning

465
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...
465
Introduction to Learning01:18

Introduction to Learning

480
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...
480
Observational Learning01:12

Observational Learning

246
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...
246
Purposive Learning01:22

Purposive Learning

169
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
169
Cognitive Learning01:21

Cognitive Learning

455
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
455
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

Training a Dynamic Growing Mixture Model for Lifelong Learning.

IEEE transactions on neural networks and learning systems·2025
Same author

Continual Unsupervised Generative Modeling.

IEEE transactions on pattern analysis and machine intelligence·2025
Same author

Lifelong Generative Adversarial Autoencoder.

IEEE transactions on neural networks and learning systems·2023
Same author

Co-attention enabled content-based image retrieval.

Neural networks : the official journal of the International Neural Network Society·2023
Same author

Characterising and dissecting human perception of scene complexity.

Cognition·2022
Same author

Dynamic Self-Supervised Teacher-Student Network Learning.

IEEE transactions on pattern analysis and machine intelligence·2022

相关实验视频

Updated: Jul 28, 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

635

终身双重生成对抗网络 协同学习

Fei Ye, Adrian G Bors

    IEEE transactions on cybernetics
    |June 1, 2023
    PubMed
    概括

    终身双生成对抗网络 (LD-GANs) 允许人工智能在不忘记以前的知识的情况下学习新概念. 这种新的方法使用教师助理模型和终身自我知识蒸,以实现高效的,持续的学习.

    科学领域:

    • 人工智能的人工智能
    • 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:

    • 先进的深度学习网络经常会在新数据上训练时忘记以前学习的信息.
    • 持续学习或终身学习 (LLL) 对于开发更有能力的AI系统至关重要.

    研究的目的:

    • 提出一个新的框架,终身双生成对抗网络 (LD-GANs),解决人工智能灾难性遗忘问题.
    • 开发一个高效的训练算法,用于在AI中不断获取知识.

    主要方法:

    • 引入了终身双生成对抗网络 (LD-GANs) 与教师和助理网络结构.
    • 提出了一种终身自我知识蒸 (LSKD) 算法,用于在任务转换期间培训LD-GAN.
    • 使用单一的区分器来评估来自两个GAN生成的图像的真实性.

    主要成果:

    • LD-GAN证明了内存效率,避免了任务学习后的参数结.
    • 该框架在无监督终身代表性学习方面表现出强的表现.
    • 将LD-GAN扩展到教师-学生网络,用于跨域数据表示同化.

    结论:

    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

    588
    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

    1.2K

    相关实验视频

    Last Updated: Jul 28, 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

    635
    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

    588
    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

    1.2K
  • 拟议的LD-GANs框架有效地实现了不遗忘的持续学习.
  • 在终身学习过程中,LSKD促进了人工智能组件之间的知识转移.
  • LD-GAN为存储效率高和持续AI开发提供了一个有前途的解决方案.