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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

609
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...
609
Observational Learning01:12

Observational Learning

390
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...
390
Associative Learning01:27

Associative Learning

682
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...
682
Force Classification01:22

Force Classification

1.8K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
1.8K
Purposive Learning01:22

Purposive Learning

238
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...
238
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

916
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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Related Experiment Videos

Towards Visual Explainable Active Learning for Zero-Shot Classification.

Shichao Jia, Zeyu Li, Nuo Chen

    IEEE Transactions on Visualization and Computer Graphics
    |September 29, 2021
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces Semantic Navigator, a visual active learning tool that aids in building zero-shot classification models. It enhances efficiency by guiding attribute selection and improving human-AI collaboration for better model performance.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Zero-shot classification enables models to classify unseen classes, but requires manual attribute definition.
    • Designing effective class-attribute matrices is challenging, time-consuming, and lacks guidance.

    Purpose of the Study:

    • To develop a visual, explainable active learning approach for efficient zero-shot classification model building.
    • To enhance human-AI collaboration in defining class attributes and improving model accuracy.

    Main Methods:

    • Proposed Semantic Navigator, a visual explainable active learning system.
    • Implemented a human-AI teaming loop with actions: ask, explain, recommend, respond.
    • Introduced a novel 'semantic map' visualization for model interpretability.
    • Developed machine-driven attribute label recommendations and interactive human feedback.

    Main Results:

    • The visual active learning approach significantly improved the efficiency of building zero-shot classification models.
    • User studies demonstrated enhanced human-AI collaboration and model performance compared to unguided methods.
    • Semantic Navigator provided better understanding of model misclassifications through visualization.

    Conclusions:

    • Semantic Navigator offers an effective solution for the tedious process of designing class-attribute matrices in zero-shot learning.
    • The human-AI interactive approach streamlines model development and improves accuracy.
    • Visual explainability and guided attribute selection are key to efficient zero-shot classification model construction.