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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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Updated: Jul 20, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Boosting Zero-Shot Learning via Contrastive Optimization of Attribute Representations.

Yu Du, Miaojing Shi, Fangyun Wei

    IEEE Transactions on Neural Networks and Learning Systems
    |August 1, 2023
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    Summary
    This summary is machine-generated.

    This study introduces a novel framework for zero-shot learning (ZSL) that enhances recognition of unseen classes by learning attribute prototypes beyond individual images. The method significantly improves state-of-the-art performance on standard ZSL benchmarks.

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    Area of Science:

    • Computer Science
    • Artificial Intelligence
    • Machine Learning

    Background:

    • Zero-shot learning (ZSL) aims to classify data from unseen categories.
    • Existing ZSL methods often focus on visual features within individual images, neglecting common attribute traits across images.
    • Attribute features are crucial but require better representation beyond single instances.

    Purpose of the Study:

    • To propose a new framework to boost ZSL performance.
    • To explicitly learn attribute prototypes beyond images.
    • To contrastively optimize these prototypes with image-level attribute features.

    Main Methods:

    • Developed a novel framework for ZSL incorporating attribute prototypes.
    • Introduced a prototype generation module (PM) to create attribute prototypes from semantics.
    • Implemented a hard-example-based contrastive optimization for attribute-level features.
    • Utilized both CNN-based and transformer-based backbones.

    Main Results:

    • The proposed method significantly improves state-of-the-art results on CUB, SUN, and AwA2 benchmarks.
    • Explicitly learning attribute prototypes enhances ZSL performance.
    • Contrastive optimization reinforces attribute-level features in the embedding space.

    Conclusions:

    • The new framework effectively addresses limitations in current ZSL approaches.
    • Learning attribute prototypes beyond images is a promising direction for ZSL.
    • The method demonstrates superior performance across multiple standard datasets.