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

Understanding Memory01:19

Understanding Memory

650
Memory is the retention of information or experiences over time, facilitated through three main processes: encoding, storage, and retrieval. Encoding is the process of inputting information into the memory system. For instance, when listening to a lecture, watching a play, reading a book, or having a conversation, the brain is actively encoding information. This initial stage involves transforming sensory input into a form that can be processed and stored by the brain. Various factors, such as...
650
Associative Learning01:27

Associative Learning

605
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...
605
Cognitive Learning01:21

Cognitive Learning

672
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...
672
Long-Term Memory01:18

Long-Term Memory

268
Long-term memory is a relatively permanent type of memory, capable of storing vast amounts of information over extended periods. Its storage capacity is generally considered unlimited.
Long-term memory can be categorized into two primary types: explicit and implicit memory. Explicit memory, also known as declarative memory, involves the conscious recollection of information that we deliberately try to remember, recall, and articulate. This type of memory encompasses specific facts, events, and...
268
System of Memory01:23

System of Memory

6.5K
Memory is categorized into three major systems: sensory memory, short-term memory (STM), and long-term memory (LTM). These systems differ in their capacity and the duration for which they can hold information. Sensory memory captures raw sensory input from the environment, holding it for just a few seconds or less. For example, on hearing a brief, loud sound, like a car horn honking, the sound seems to linger in the mind for a moment even after it stops. This is an instance of sensory memory...
6.5K
Chunking and Rehearsal in Sensory Memory01:22

Chunking and Rehearsal in Sensory Memory

305
Improving short-term memory can be achieved through techniques like chunking and rehearsal. Chunking involves organizing information into larger, more manageable units. This technique is particularly useful for information that exceeds the typical memory span of between five and nine items. For instance, logging into an online account with a password like "ta89vq0179gz" involves grouping letters and numbers into three chunks—ta89, vq01, and 79gz. It makes large amounts of...
305

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

Chronic Subdural Hematoma and the Longitudinal Trajectory of Intrinsic Capacity: A Cross-Lagged Panel Network Analysis.

The Journal of craniofacial surgery·2026
Same author

Does intraoperative anesthesia handovers associated with adverse outcomes? A systematic review and meta-analysis.

Frontiers in medicine·2026
Same author

Cardiac arrest due to tamponade during secondary-stage endovascular stent implantation in a patient with DeBakey type I dissection: a case report and literature review.

Frontiers in medicine·2026
Same author

The Wear Resistance of Reinforced Coatings Fabricated by Three Different Processes on High-Density Tungsten Alloy.

Materials (Basel, Switzerland)·2026
Same author

PANoptosis nexus in ischemia-reperfusion injury: from integrated cell death mechanisms to novel therapeutic opportunities.

Frontiers in immunology·2026
Same author

Retinol saturase in the mitochondria antagonizes IDH2 and GLUD1 acetylation to mediate heart repair.

Proceedings of the National Academy of Sciences of the United States of America·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
Same journal

Hierarchical Semantic Concept Modeling for Generalizable Myocardial Pathology Segmentation on Multisequence CMR Images.

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

相关实验视频

Updated: Sep 19, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

CKDF-V2:有效地缓解小记忆持续学习的表示转移.

Kunchi Li, Hongyang Chen, Jun Wan

    IEEE transactions on neural networks and learning systems
    |June 2, 2025
    PubMed
    概括
    此摘要是机器生成的。

    持续学习 (CL) 模型面临着灾难性的遗忘,因为代表性转移. 在CKDF-V2中,特征提升校准 (FBC) 和区块知识蒸 (BWKD) 提高了特征可转移性,并解决了数据不平衡,以提高CL性能.

    更多相关视频

    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
    09:01

    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

    Published on: July 8, 2015

    12.7K
    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
    05:15

    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

    Published on: February 19, 2018

    10.9K

    相关实验视频

    Last Updated: Sep 19, 2025

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.7K
    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents
    09:01

    The Double-H Maze: A Robust Behavioral Test for Learning and Memory in Rodents

    Published on: July 8, 2015

    12.7K
    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition
    05:15

    The Spatial Memory Game: Testing the Relationship Between Spatial Language, Object Knowledge, and Spatial Cognition

    Published on: February 19, 2018

    10.9K

    科学领域:

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

    背景情况:

    • 持续学习 (CL) 模型在遇到分布外数据时,与灾难性遗忘作斗争.
    • 在模型更新过程中发生了表示转移 (RS),降低了以前学习的任务的性能.
    • 在CL中,有限的内存加剧了旧与新类之间的数据不平衡.

    研究的目的:

    • 提出新的方法,特征提升校准 (FBC) 和区块化知识蒸 (BWKD),以减轻灾难性遗忘和持续学习中的表示转移.
    • 引入CKDF-V2,一个两阶段的培训框架,整合FBC和BWKD,以加强持续学习.
    • 通过使用任务令牌扩展,为视觉转换器 (ViT) 调整 CKDF-V2.

    主要方法:

    • 功能提升校准 (FBC):一个扩展模块识别并利用错过的关键功能来校准旧表示,增强功能可转移性.
    • 区块化知识蒸 (BWKD):软max层根据分别蒸的类频率划分为区块,有效解决数据不平衡问题.
    • CKDF-V2框架:结合FBC和BWKD的两阶段培训方法,并使用任务令牌扩展扩展ViT集成.

    主要成果:

    • 通过利用错过的特征,FBC成功地校准了表示,从而减轻了表示转移.
    • BWKD有效地解决了与持续学习场景固有的数据不平衡问题.
    • 拟议的CKDF-V2框架适用于CNN和ViT,在多个持续学习基准中取得了良好的结果.

    结论:

    • 结合FBC和BWKD的CKDF-V2为持续学习挑战提供了强大的解决方案,显著提高了模型性能.
    • 与ViT的集成证明了框架在现代深度学习架构中的多功能性和有效性.
    • 提出的方法有效地打击灾难性遗忘和表示转移,为更稳定的持续学习系统铺平道路.