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Related Concept Videos

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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Generalization, Discrimination, and Extinction01:24

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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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Introduction to Learning01:18

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Related Experiment Video

Updated: Sep 30, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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From Big to Small: Adaptive Learning to Partial-Set Domains.

Zhangjie Cao, Kaichao You, Ziyang Zhang

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    This study introduces Partial Domain Adaptation (PDA) for machine learning, enabling knowledge transfer between datasets with different class sets. The proposed Selective Adversarial Network (SAN++) effectively adapts models by selectively focusing on transferable knowledge.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Vision

    Background:

    • Domain adaptation facilitates knowledge transfer from labeled source to unlabeled target domains, but is limited by the requirement of identical class spaces.
    • Deep pre-trained models offer rich knowledge for downstream tasks, motivating adaptation from large-scale to small-scale domains.
    • Partial-set domains, where source classes encompass target classes, present unique challenges for traditional domain adaptation.

    Purpose of the Study:

    • To introduce and theoretically analyze Partial Domain Adaptation (PDA), a paradigm relaxing the identical class space constraint.
    • To develop a novel method, Selective Adversarial Network (SAN and SAN++), for effective knowledge transfer in partial-set domain adaptation.
    • To address the challenge of adapting large-scale pre-trained models to smaller-scale target domains with differing class distributions.

    Main Methods:

    • Theoretical analysis of PDA to identify the importance of transferable probability estimation for classes and instances.
    • Proposal of Selective Adversarial Network (SAN and SAN++) employing a bi-level selection strategy and adversarial adaptation.
    • Alternating estimation of transferable probabilities to up-weight classes and instances for supervised training, self-training, and adversarial adaptation.

    Main Results:

    • The proposed Selective Adversarial Network (SAN++) demonstrates superior performance compared to existing domain adaptation methods.
    • Experiments on standard partial-set datasets and superclass tasks validate the effectiveness of SAN++.
    • The bi-level selection strategy effectively identifies and leverages transferable knowledge across domains with partial class overlap.

    Conclusions:

    • Partial Domain Adaptation (PDA) is a viable and important learning paradigm for real-world applications where class spaces differ.
    • Selective Adversarial Network (SAN++) provides an effective solution for PDA by intelligently selecting and adapting knowledge.
    • The findings highlight the potential of adapting large-scale models to smaller, related domains even with partial class set differences.