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相关概念视频

Observational Learning01:12

Observational Learning

166
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...
166
Cognitive Learning01:21

Cognitive Learning

237
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...
237
Associative Learning01:27

Associative Learning

340
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...
340
Introduction to Learning01:18

Introduction to Learning

364
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...
364
Purposive Learning01:22

Purposive Learning

118
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...
118
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

528
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
528

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Updated: Jun 25, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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PAMK:为持续的零射击学习提供原型增强型多教师知识传输网络.

Junxin Lu, Shiliang Sun

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |May 24, 2024
    PubMed
    概括
    此摘要是机器生成的。

    本研究介绍了PAMK,这是一个用于持续零射击学习 (CZSL) 的新型网络,它平衡了旧任务稳定性和新任务可塑性. 帕姆克有效地减少了遗忘和负面转移,提高了未见任务的性能.

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    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:

    • 持续的零射击学习 (CZSL) 旨在学习新的任务,而不忘记以前的任务.
    • 现有的CZSL方法经常存在负转移,阻碍了新任务的泛化.
    • 这是由于过度依赖旧知识和模型可塑性降低.

    研究的目的:

    • 为CZSL提出PAMK,一个原型增强的多教师知识传输网络.
    • 为了平衡旧任务的识别稳定性和新任务的泛化可塑性.
    • 克服灾难性遗忘和CZSL的负转移问题.

    主要方法:

    • PAMK使用了原型增强对比生成 (PACG) 模块和多教师知识传递 (MKT) 模块.
    • PACG采用了持续原型增强策略和语义视觉对比损失.
    • 通过多教师转移,MKT促进了从旧任务到新任务的知识积累.

    主要成果:

    • 在各种CZSL设置中,PAMK在各种CZSL设置中表现出优于最先进的方法的性能.
    • 在没有任务的CZSL设置中观察到平均波精度的显著增长.
    • 在CUB,AWA1和AWA2数据集中分别实现了3.28%,3.09%和3.71%的改进.

    结论:

    • 帕姆克有效地缓解了CZSL的灾难性遗忘和负面转移问题.
    • 提出的方法提高了旧任务的稳定性和新任务的可塑性.
    • 帕姆克为强大而适应的持续学习系统提供了一个有前途的解决方案.