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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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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Dual-Correction-Adaptation Network for Noisy Knowledge Transfer.

Yunyun Wang, Weiwen Zheng, Qinghao Li

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    |October 19, 2023
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    Summary
    This summary is machine-generated.

    This study introduces DualCAN, a novel dual-directional transfer method for unsupervised domain adaptation. It enables mutual learning between domains, significantly improving performance on noisy datasets.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Unsupervised domain adaptation (UDA) typically uses single-directional transfer from source to target domains.
    • The reverse transfer from target to source, crucial for a virtuous learning cycle, has been underexplored.
    • Existing methods struggle with noise amplification due to model bias and inherent domain noise.

    Purpose of the Study:

    • To introduce a novel dual-directional transfer approach for unsupervised domain adaptation.
    • To address noise amplification issues in cross-domain learning.
    • To enhance learning in both source and target domains through mutual adaptation and correction.

    Main Methods:

    • Propose the DualCAN (Dual-Correction-Adaptation Network).
    • Implement a cyclical adaptation and correction process between source and target domains.
    • Leverage target domain knowledge to refine source domain adaptation and vice-versa.

    Main Results:

    • Demonstrate significant performance gains across various UDA tasks.
    • Achieve substantial improvements on extremely noisy datasets, e.g., +10% on Office-31 with 40% label corruption.
    • Validate the effectiveness of the dual-directional transfer and correction mechanism.

    Conclusions:

    • DualCAN represents the first attempt at dual-directional adaptation in UDA.
    • The proposed method effectively mitigates noise and boosts learning in both domains.
    • This approach offers a promising direction for robust and efficient domain adaptation.