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Associative Learning01:27

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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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Related Experiment Video

Updated: Jan 1, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

942

Self-Paced Collaborative and Adversarial Network for Unsupervised Domain Adaptation.

Weichen Zhang, Dong Xu, Wanli Ouyang

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |December 28, 2019
    PubMed
    Summary

    This study introduces Collaborative and Adversarial Network (CAN) for unsupervised domain adaptation. It enhances target domain discriminability and reduces domain mismatch, achieving state-of-the-art results in object and action recognition tasks.

    Related Experiment Videos

    Last Updated: Jan 1, 2026

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
    03:14

    Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

    Published on: December 6, 2024

    942

    Area of Science:

    • Computer Vision
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Unsupervised domain adaptation is crucial for applying models to new data distributions.
    • Existing methods struggle with domain shift and preserving target domain discriminability.
    • Neural network feature representation is key to bridging domain gaps.

    Purpose of the Study:

    • To propose a novel unsupervised domain adaptation approach, Collaborative and Adversarial Network (CAN).
    • To enhance feature representation for improved target domain discriminability and reduced domain mismatch.
    • To extend the approach for video action recognition using a two-stream architecture.

    Main Methods:

    • Developed domain-collaborative and domain-adversarial learning strategies within CAN.
    • Designed a training scheme for automatic learning of domain-specific and domain-invariant features.
    • Introduced Self-Paced CAN (SPCAN) for progressive selection of pseudo-labeled target samples.
    • Extended SPCAN to Two-stream SPCAN (TS-SPCAN) for video action recognition, enabling inter-stream information exchange.

    Main Results:

    • CAN effectively learns domain-specific and domain-invariant features.
    • SPCAN and TS-SPCAN significantly improve discriminability in target domains.
    • Achieved state-of-the-art performance on benchmark datasets for object recognition (Office-31, ImageCLEF-DA, VISDA-2017).
    • Demonstrated superior results on video action recognition tasks (UCF101-10, HMDB51-10).

    Conclusions:

    • The proposed CAN framework offers an effective approach to unsupervised domain adaptation.
    • SPCAN and TS-SPCAN provide significant improvements by leveraging self-paced learning and two-stream cooperation.
    • The methods are broadly applicable and achieve leading performance across diverse recognition tasks.