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相关概念视频

Purposive Learning01:22

Purposive Learning

93
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...
93
Observational Learning01:12

Observational Learning

106
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...
106
Cognitive Learning01:21

Cognitive Learning

108
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...
108
Associative Learning01:27

Associative Learning

253
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...
253
Language Development01:22

Language Development

289
Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
The critical period for language acquisition suggests that the ability to acquire language is at its peak early in life. As people age, this proficiency decreases. Language development begins very...
289
Introduction to Learning01:18

Introduction to Learning

309
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...
309

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CoLeCLIP:通过联合任务提示符和词汇学习进行开放领域的持续学习.

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    此摘要是机器生成的。

    本研究介绍了CoLeCLIP,这是一种在开放域视觉语言模型 (VLMs) 中持续学习 (CL) 的新方法. CoLeCLIP有效地处理各种数据集,并防止知识被遗忘,在挑战增量学习场景中取得最先进的结果.

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    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) 中的持续学习 (CL) 对开放世界应用,如人工智能助理和机器人技术至关重要.
    • 现有的CL研究主要针对单个领域内的封闭式场景,忽视了多样化,不断变化的数据集的复杂性.
    • 大型预训练VLM,如CLIP,提供强大的零射击能力,但在动态环境中扎着灾难性遗忘.

    研究的目的:

    • 为VLMs开发一个强大的开放领域的持续学习框架.
    • 解决阶级相关性,域间隙和知识遗忘在动态,多域环境中的挑战.
    • 在现实世界应用中增强VLM的适应性和终身学习能力.

    主要方法:

    • 介绍了CoLeCLIP,这是基于CLIP架构的开放域CL的新方法.
    • 使用任务提示的联合学习和跨领域的类词汇来管理各种数据流.
    • 在任务增量和类增量学习设置下,对11个不同的域数据集进行了评估.

    主要成果:

    • CoLeCLIP在开放领域的持续学习任务中表现出卓越的表现.
    • 在任务增量和类增量学习环境中取得了新的最先进的结果.
    • 成功地缓解了灾难性的遗忘,并适应了新的类和领域.

    结论:

    • CoLeCLIP为VLMs提供了开放领域的持续学习的重大进展.
    • 提出的方法有效地解决了关键挑战,包括领域转移和知识保留.
    • 在开放的环境中,CoLeCLIP为更具能力和适应性的人工智能系统铺平了道路.