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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

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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...
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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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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.
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Observational Learning01:12

Observational Learning

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

Multi-input and Multi-variable systems

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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.
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Avoidance Learning and Learned Helplessness01:14

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
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Related Experiment Video

Updated: Oct 13, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

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ZeroNAS: Differentiable Generative Adversarial Networks Search for Zero-Shot Learning.

Caixia Yan, Xiaojun Chang, Zhihui Li

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |November 11, 2021
    PubMed
    Summary

    This study introduces ZeroNAS, a novel neural architecture search (NAS) method for generative adversarial networks (GANs) to improve zero-shot learning (ZSL). ZeroNAS automatically designs GAN architectures, enhancing performance across diverse datasets for ZSL and generalized ZSL tasks.

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    Last Updated: Oct 13, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.4K

    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Generative Adversarial Networks (GANs) have advanced zero-shot learning (ZSL), but rely on hand-crafted architectures.
    • Existing GAN models for ZSL lack guaranteed performance across varied datasets and scenarios.
    • Neural Architecture Search (NAS) offers a potential solution for optimizing ZSL models.

    Purpose of the Study:

    • To introduce the first NAS technique specifically designed for the zero-shot learning domain.
    • To develop an automated method for discovering effective GAN architectures for ZSL.
    • To address the limitations of manually designed GANs in ZSL tasks.

    Main Methods:

    • Propose ZeroNAS, a differentiable GAN architecture search method.
    • Define a specialized search space tailored for zero-shot learning.
    • Employ adversarial training within a min-max game to jointly search generator and discriminator architectures.

    Main Results:

    • ZeroNAS successfully discovers high-performing GAN architectures for ZSL.
    • Experimental results on four benchmark datasets show favorable comparisons against state-of-the-art ZSL and GZSL methods.
    • The automated approach demonstrates robustness across diverse datasets.

    Conclusions:

    • ZeroNAS effectively automates GAN architecture design for zero-shot learning.
    • The proposed method achieves superior performance in both ZSL and generalized ZSL (GZSL).
    • This work highlights the potential of NAS in advancing ZSL research.