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相关概念视频

Cognitive Learning01:21

Cognitive Learning

1.0K
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...
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Observational Learning01:12

Observational Learning

824
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...
824
Introduction to Learning01:18

Introduction to Learning

931
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...
931
Purposive Learning01:22

Purposive Learning

438
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...
438
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

385
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
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Associative Learning01:27

Associative Learning

1.2K
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...
1.2K

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相关实验视频

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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视觉语言模型的多阶段知识整合,用于持续学习.

Hongsheng Zhang, Zhong Ji, Jingren Liu

    IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
    |January 13, 2026
    PubMed
    概括

    视觉语言模型 (VLMs) 可以通过多阶段知识集成 (MulKI) 改进,以实现持续学习. MulKI提高了对新数据的适应性,同时保留了现有知识,克服了当前蒸方法的局限性.

    科学领域:

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

    背景情况:

    • 视觉语言模型 (VLMs) 在零射击任务中表现出色,但在专门的看不见的数据上扎.
    • 持续学习 (CL) 旨在在没有重新培训的情况下使VLM适应新数据,但面临着灾难性的遗忘和概括问题.
    • 在VLM中对CL的现有蒸方法受到单个教师范式和多式联络数据的不充分使用的限制,增加了开销.

    研究的目的:

    • 解决目前基于蒸的VLM持续学习的局限性.
    • 提出一个新的网络,多阶段知识整合 (MulKI),灵感来自知识整合理论 (KIT).
    • 增强VLM适应不断变化的数据分布,同时保持零射击能力.

    主要方法:

    • 开发了多阶段知识整合 (MulKI) 网络,通过四个阶段模拟人类的学习:诱导,添加,区分和建立联系.
    • 利用了跨模式对齐的原型,并构建了细粒度的模式内和模式间关系.
    • 从两个教师模型中适应性地区分和重新权衡知识,跨任务整合先前和新知识.

    主要成果:

    • 在持续学习过程中,MulKI在保持零射击能力方面取得了显著的改进.
    • 该方法有效地支持各种下游任务的适应.

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  • MulKI减轻了灾难性的遗忘和一般化的遗忘,这也是CL固有的挑战.
  • 结论:

    • 拟议的MulKI网络为视觉语言模型提供了一种有效的持续学习方法.
    • 通过模拟人类的学习过程,MulKI成功地整合了知识,克服了现有方法的局限性.
    • 这项工作显示了VLM适应动态,不断变化的数据环境的前景.