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  1. Home
  3. Instance-specific Model Perturbation Improves Generalized Zero-shot Learning.
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  3. Instance-specific Model Perturbation Improves Generalized Zero-shot Learning.

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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Instance-Specific Model Perturbation Improves Generalized Zero-Shot Learning.

Guanyu Yang1, Kaizhu Huang2, Rui Zhang3

  • 1Data Science Research Center, Duke Kunshan University, Kunshan, 215316, China guanyu.yang@dukekunshan.edu.cn.

Neural Computation
|March 8, 2024

View abstract on PubMed

Summary
This summary is machine-generated.

This study introduces a novel adversarial training method to improve generalized zero-shot learning (GZSL). The approach enhances model accuracy by making predictions more sensitive to unseen classes while maintaining robustness for seen classes.

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Machine Learning

Background:

  • Generalized zero-shot learning (GZSL) aims to classify both seen and unseen data categories.
  • Existing GZSL models often overfit to seen classes, misclassifying unseen data.
  • A key challenge is balancing recognition of seen classes with sensitivity to novel, unseen classes.

Purpose of the Study:

  • To develop a robust framework for generalized zero-shot learning (GZSL).
  • To mitigate the tendency of GZSL models to overfit training data and misclassify unseen classes.
  • To enhance the accurate prediction of both seen and unseen classes in ZSL tasks.

Main Methods:

  • Implemented a parameter-wise adversarial training process for robust recognition of seen classes.
  • Introduced a novel model perturbation mechanism during testing to increase sensitivity to unseen classes.
  • Computed parameter perturbations from multiple individuals simultaneously to avoid extreme, detrimental effects on predictions.

    • Main Results:

    • The proposed framework demonstrated effective improvement on zero-shot learning methods utilizing learned metrics.
    • Adversarial perturbation successfully biased predictions towards unseen classes during testing.
    • Robust training ensured that predictions for seen classes remained largely unaffected, indicating improved model stability.

    Conclusions:

    • The developed adversarial training and perturbation strategy significantly enhances GZSL performance.
    • The method effectively addresses the overfitting issue in GZSL by improving unseen class recognition.
    • This approach offers a promising direction for advancing zero-shot learning capabilities in AI.