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Related Concept Videos

Observational Learning01:12

Observational Learning

751
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...
751
Introduction to Learning01:18

Introduction to Learning

847
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...
847
Associative Learning01:27

Associative Learning

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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...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.2K
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...
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Purposive Learning01:22

Purposive Learning

383
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...
383
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.4K
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...
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Related Experiment Videos

Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning.

Jiechao Guan, Zhiwu Lu, Tao Xiang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |January 16, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a new method for zero-shot learning (ZSL) and few-shot learning (FSL) by synthesizing unseen class data and using competitive bidirectional projection learning (BPL). The approach enhances model robustness and achieves state-of-the-art results on both ZSL and FSL tasks.

    Related Experiment Videos

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Zero-shot learning (ZSL) relies on projection functions between feature and semantic spaces.
    • A significant challenge in ZSL is the domain gap between seen and unseen classes.
    • Robust projection learning is crucial for effective ZSL.

    Purpose of the Study:

    • To develop a robust projection function for zero-shot learning (ZSL) that addresses the domain gap.
    • To propose a novel semantic data synthesis strategy for generating unseen class data.
    • To extend the proposed ZSL model to few-shot learning (FSL).

    Main Methods:

    • A novel semantic data synthesis strategy using class prototypes to perturb seen data for unseen class generation.
    • Competitive bidirectional projection learning (BPL) model to handle ambiguities in synthesized data.
    • Extension to few-shot learning (FSL) via semantic feature synthesis and competitive BPL.

    Main Results:

    • The proposed semantic data synthesis effectively generates unseen class data.
    • The competitive BPL model robustly utilizes ambiguous synthesized data for projection learning.
    • The ZSL model successfully extends to FSL, achieving state-of-the-art performance.

    Conclusions:

    • The developed approach effectively bridges the domain gap in ZSL through data synthesis and robust projection learning.
    • The competitive BPL framework demonstrates superior performance in handling synthesized data.
    • The model's adaptability to few-shot learning further validates its effectiveness and versatility.